UA-DETRAC: A new benchmark and protocol for multi-object detection and tracking
Effective multi-object tracking (MOT) methods have been developed in recent years for a wide range of applications including visual surveillance and behavior understanding. Existing performance evaluations of MOT methods usually separate the tracking step from the detection step by using one single...
Saved in:
| Published in | Computer vision and image understanding Vol. 193; p. 102907 |
|---|---|
| Main Authors | , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier Inc
01.04.2020
|
| Subjects | |
| Online Access | Get full text |
Cover
Loading…
| Abstract | Effective multi-object tracking (MOT) methods have been developed in recent years for a wide range of applications including visual surveillance and behavior understanding. Existing performance evaluations of MOT methods usually separate the tracking step from the detection step by using one single predefined setting of object detection for comparisons. In this work, we propose a new University at Albany DEtection and TRACking (UA-DETRAC) dataset for comprehensive performance evaluation of MOT systems especially on detectors. The UA-DETRAC benchmark dataset consists of 100 challenging videos captured from real-world traffic scenes (over 140,000 frames with rich annotations, including illumination, vehicle type, occlusion, truncation ratio, and vehicle bounding boxes) for multi-object detection and tracking. We evaluate complete MOT systems constructed from combinations of state-of-the-art object detection and tracking methods. Our analysis shows the complex effects of detection accuracy on MOT system performance. Based on these observations, we propose effective and informative evaluation metrics for MOT systems that consider the effect of object detection for comprehensive performance analysis.
•New large scale dataset for both detection and multi-object tracking evaluation.•New protocol and evaluation metrics for multi-object tracking.•Comprehensive evaluation of complete multi-object tracking systems. |
|---|---|
| AbstractList | Effective multi-object tracking (MOT) methods have been developed in recent years for a wide range of applications including visual surveillance and behavior understanding. Existing performance evaluations of MOT methods usually separate the tracking step from the detection step by using one single predefined setting of object detection for comparisons. In this work, we propose a new University at Albany DEtection and TRACking (UA-DETRAC) dataset for comprehensive performance evaluation of MOT systems especially on detectors. The UA-DETRAC benchmark dataset consists of 100 challenging videos captured from real-world traffic scenes (over 140,000 frames with rich annotations, including illumination, vehicle type, occlusion, truncation ratio, and vehicle bounding boxes) for multi-object detection and tracking. We evaluate complete MOT systems constructed from combinations of state-of-the-art object detection and tracking methods. Our analysis shows the complex effects of detection accuracy on MOT system performance. Based on these observations, we propose effective and informative evaluation metrics for MOT systems that consider the effect of object detection for comprehensive performance analysis.
•New large scale dataset for both detection and multi-object tracking evaluation.•New protocol and evaluation metrics for multi-object tracking.•Comprehensive evaluation of complete multi-object tracking systems. |
| ArticleNumber | 102907 |
| Author | Cai, Zhaowei Lei, Zhen Qi, Honggang Lyu, Siwei Du, Dawei Chang, Ming-Ching Yang, Ming-Hsuan Lim, Jongwoo Wen, Longyin |
| Author_xml | – sequence: 1 givenname: Longyin surname: Wen fullname: Wen, Longyin organization: JD Finance America Corporation, Mountain View, CA, USA – sequence: 2 givenname: Dawei surname: Du fullname: Du, Dawei organization: Computer Science Department, University at Albany, State University of New York, Albany, NY, USA – sequence: 3 givenname: Zhaowei surname: Cai fullname: Cai, Zhaowei organization: Department of Electrical and Computer Engineering, University of California, San Diego, CA, USA – sequence: 4 givenname: Zhen surname: Lei fullname: Lei, Zhen organization: National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China – sequence: 5 givenname: Ming-Ching surname: Chang fullname: Chang, Ming-Ching organization: Computer Science Department, University at Albany, State University of New York, Albany, NY, USA – sequence: 6 givenname: Honggang surname: Qi fullname: Qi, Honggang organization: School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing, China – sequence: 7 givenname: Jongwoo surname: Lim fullname: Lim, Jongwoo organization: Division of Computer Science and Engineering, Hanyang University, Seoul, Korea – sequence: 8 givenname: Ming-Hsuan surname: Yang fullname: Yang, Ming-Hsuan organization: School of Engineering, University of California at Merced, CA, USA – sequence: 9 givenname: Siwei surname: Lyu fullname: Lyu, Siwei email: slyu@albany.edu organization: Computer Science Department, University at Albany, State University of New York, Albany, NY, USA |
| BookMark | eNp9kE1OwzAQRr0oEm3hAqx8gRQ7TuIGsYlK-ZEqVULt2nLsMThN48pxi7g9DmXFoqvRjOaN5nsTNOpcBwjdUTKjhBb3zUyd7HGWknQYpCXhIzSmhPOE0Sy9RpO-bwihNCvpGK23VfK03LxXiwdc4Q6-cA2d-txLv8Oy0_jgXXDKtdg4j_fHNtjE1Q2ogDWEWKzrfveCl2pnu48bdGVk28PtX52i7fNys3hNVuuXt0W1ShQjJCR1DZzwjAHNCddSsoJmuuCqzE0OaQYlI8Cl0lSbOZXZvOTU5BpiZ0pW84JNUXq-q7zrew9GHLyNX38LSsSgQTRi0CAGDeKsIULzf5CyQQ4Z4vu2vYw-nlGIoU4WvOiVjaZAWx81CO3sJfwHfjl8ew |
| CitedBy_id | crossref_primary_10_1109_ACCESS_2024_3397013 crossref_primary_10_1007_s11042_023_14839_7 crossref_primary_10_1109_ACCESS_2021_3064603 crossref_primary_10_1016_j_engappai_2024_107972 crossref_primary_10_1155_2023_7974201 crossref_primary_10_1109_TITS_2023_3304288 crossref_primary_10_1007_s44267_023_00030_x crossref_primary_10_3390_app14167071 crossref_primary_10_1016_j_neunet_2024_106539 crossref_primary_10_1109_OJITS_2025_3538037 crossref_primary_10_1038_s41598_024_70695_x crossref_primary_10_1155_2022_4488400 crossref_primary_10_1109_TII_2023_3316184 crossref_primary_10_1177_03611981241246255 crossref_primary_10_32628_CSEIT2174106 crossref_primary_10_1109_ACCESS_2022_3143365 crossref_primary_10_1016_j_ssci_2024_106563 crossref_primary_10_1049_cvi2_70010 crossref_primary_10_1007_s11263_020_01375_2 crossref_primary_10_1007_s00138_025_01668_x crossref_primary_10_1007_s11042_023_14661_1 crossref_primary_10_1007_s13735_025_00355_x crossref_primary_10_1007_s10462_025_11186_x crossref_primary_10_1117_1_JEI_31_5_051602 crossref_primary_10_1007_s11263_022_01711_8 crossref_primary_10_1109_ACCESS_2021_3060821 crossref_primary_10_3390_s23042208 crossref_primary_10_1016_j_knosys_2023_111325 crossref_primary_10_1016_j_cviu_2022_103433 crossref_primary_10_1016_j_dsp_2024_104507 crossref_primary_10_1007_s13198_024_02311_0 crossref_primary_10_1016_j_engappai_2024_109138 crossref_primary_10_1109_TMC_2024_3465434 crossref_primary_10_1007_s11042_023_16373_y crossref_primary_10_1007_s11042_023_16102_5 crossref_primary_10_1016_j_neucom_2024_127635 crossref_primary_10_3390_s23239295 crossref_primary_10_1007_s11831_022_09764_1 crossref_primary_10_1117_1_JEI_33_6_063008 crossref_primary_10_1142_S0218213023500550 crossref_primary_10_1007_s13748_022_00290_6 crossref_primary_10_1049_itr2_12227 crossref_primary_10_1088_2631_8695_ad507d crossref_primary_10_1007_s42979_022_01130_z crossref_primary_10_1117_1_JEI_32_6_063023 crossref_primary_10_1109_TIV_2023_3318070 crossref_primary_10_1007_s11042_022_13469_9 crossref_primary_10_1109_OJITS_2025_3532389 crossref_primary_10_1109_TPAMI_2022_3168781 crossref_primary_10_1007_s00530_024_01407_8 crossref_primary_10_3390_rs14163853 crossref_primary_10_1016_j_eswa_2023_121020 crossref_primary_10_1109_TWC_2024_3519325 crossref_primary_10_1109_OJITS_2022_3147744 crossref_primary_10_1109_ACCESS_2025_3538556 crossref_primary_10_3390_app12199408 crossref_primary_10_1109_TCDS_2021_3073694 crossref_primary_10_3390_s24010249 crossref_primary_10_1109_TVT_2021_3065250 crossref_primary_10_3390_s23104832 crossref_primary_10_1007_s11760_024_03007_5 crossref_primary_10_1109_TITS_2022_3225709 crossref_primary_10_1016_j_patcog_2022_109107 crossref_primary_10_1007_s11042_024_19145_4 crossref_primary_10_1109_TMM_2021_3130414 crossref_primary_10_3390_a13050114 crossref_primary_10_1007_s10489_020_01815_z crossref_primary_10_1145_3624478 crossref_primary_10_1109_ACCESS_2022_3184031 crossref_primary_10_1109_MITS_2022_3201400 crossref_primary_10_1109_TITS_2024_3360875 crossref_primary_10_3390_telecom4030024 crossref_primary_10_1109_TCSVT_2022_3168279 crossref_primary_10_1016_j_patcog_2024_111151 crossref_primary_10_1145_3645107 crossref_primary_10_3390_app131810225 crossref_primary_10_1007_s11042_023_15981_y crossref_primary_10_1109_OJEMB_2023_3288470 crossref_primary_10_1109_ACCESS_2025_3534098 crossref_primary_10_1007_s11554_021_01131_w crossref_primary_10_1049_cit2_12406 crossref_primary_10_3390_s21134564 crossref_primary_10_1109_TITS_2024_3359755 crossref_primary_10_1109_JSEN_2024_3449329 crossref_primary_10_1117_1_JEI_31_4_043008 crossref_primary_10_1109_TVT_2023_3332132 crossref_primary_10_3390_app12157473 crossref_primary_10_1007_s11042_023_17397_0 crossref_primary_10_1109_LRA_2022_3199026 crossref_primary_10_1109_TITS_2023_3316691 crossref_primary_10_1109_COMST_2023_3323091 crossref_primary_10_1109_MNET_2024_3398724 crossref_primary_10_1186_s13673_020_00219_9 crossref_primary_10_3390_s24248126 crossref_primary_10_1007_s12293_025_00440_y crossref_primary_10_3390_rs14215513 crossref_primary_10_1016_j_patcog_2024_110721 crossref_primary_10_1109_ACCESS_2022_3160424 crossref_primary_10_1038_s41598_023_50306_x crossref_primary_10_1049_tje2_70056 crossref_primary_10_1016_j_eswa_2022_119366 crossref_primary_10_3390_app14052124 crossref_primary_10_1016_j_jksuci_2023_101657 crossref_primary_10_1016_j_neucom_2024_127328 crossref_primary_10_1016_j_patcog_2022_108847 crossref_primary_10_1109_TKDE_2020_3048606 crossref_primary_10_3390_app14167383 crossref_primary_10_1007_s00521_023_08963_6 crossref_primary_10_1016_j_compeleceng_2021_107366 crossref_primary_10_1038_s41598_024_82356_0 crossref_primary_10_1109_ACCESS_2021_3053546 crossref_primary_10_1016_j_eswa_2024_125872 crossref_primary_10_1007_s11042_023_16243_7 crossref_primary_10_1016_j_aei_2021_101429 crossref_primary_10_1109_TITS_2024_3435775 crossref_primary_10_1145_3632181 crossref_primary_10_1016_j_cag_2022_03_012 crossref_primary_10_1007_s12204_021_2349_6 crossref_primary_10_1117_1_JEI_33_2_023041 crossref_primary_10_3390_s23083852 crossref_primary_10_1007_s42421_020_00026_9 crossref_primary_10_1109_ACCESS_2021_3139896 crossref_primary_10_1109_ACCESS_2021_3069216 crossref_primary_10_3390_electronics12234720 crossref_primary_10_1007_s11042_023_16337_2 crossref_primary_10_12720_jait_14_6_1261_1272 crossref_primary_10_3390_s24041182 crossref_primary_10_1109_ACCESS_2022_3207282 crossref_primary_10_1061_JTEPBS_TEENG_8341 crossref_primary_10_1109_TITS_2023_3264573 crossref_primary_10_1109_TPAMI_2021_3119563 crossref_primary_10_1109_TITS_2022_3206709 crossref_primary_10_1109_TITS_2023_3315222 crossref_primary_10_1016_j_eswa_2024_124661 crossref_primary_10_1109_TKDE_2021_3094997 crossref_primary_10_1109_JSTARS_2024_3443268 crossref_primary_10_3390_s23020724 crossref_primary_10_1109_ACCESS_2020_3038643 crossref_primary_10_1007_s11227_024_06121_w crossref_primary_10_1109_ACCESS_2023_3345220 crossref_primary_10_1109_ACCESS_2024_3507713 crossref_primary_10_1016_j_eswa_2023_121209 crossref_primary_10_3390_math10050733 crossref_primary_10_3390_s21041116 crossref_primary_10_1016_j_neucom_2022_04_087 crossref_primary_10_1007_s11042_023_14812_4 crossref_primary_10_1007_s10489_020_02171_8 crossref_primary_10_1016_j_isprsjprs_2022_07_022 crossref_primary_10_1109_TITS_2022_3149277 crossref_primary_10_1007_s42154_023_00258_9 crossref_primary_10_1016_j_eswa_2024_125653 crossref_primary_10_1080_21680566_2024_2380915 crossref_primary_10_1186_s40537_022_00581_8 crossref_primary_10_1109_TGRS_2023_3271645 crossref_primary_10_3390_app13063995 crossref_primary_10_1007_s11554_024_01457_1 crossref_primary_10_1007_s12204_021_2348_7 crossref_primary_10_1109_ACCESS_2023_3347791 crossref_primary_10_3390_electronics13030541 crossref_primary_10_1109_TITS_2023_3258683 crossref_primary_10_3390_nano13243139 crossref_primary_10_1109_TITS_2023_3264796 crossref_primary_10_1016_j_ins_2021_03_027 crossref_primary_10_3390_su15031950 crossref_primary_10_1016_j_aei_2025_103165 crossref_primary_10_1016_j_imavis_2020_103932 crossref_primary_10_1007_s11263_020_01393_0 crossref_primary_10_32604_cmc_2023_038910 crossref_primary_10_1007_s11263_022_01629_1 crossref_primary_10_1016_j_eswa_2022_118992 crossref_primary_10_1007_s00530_024_01661_w crossref_primary_10_1109_TIP_2023_3280010 crossref_primary_10_1109_ACCESS_2022_3144153 crossref_primary_10_1002_dac_5593 crossref_primary_10_1049_ipr2_13207 crossref_primary_10_1186_s10033_023_00962_x crossref_primary_10_11834_jig_220026 crossref_primary_10_3390_s24227138 crossref_primary_10_14778_3598581_3598599 crossref_primary_10_1016_j_neucom_2023_126558 crossref_primary_10_1177_09544070241287582 crossref_primary_10_1109_TNNLS_2021_3136907 crossref_primary_10_1007_s11042_023_17949_4 crossref_primary_10_1051_itmconf_20235402006 crossref_primary_10_1016_j_inffus_2024_102429 crossref_primary_10_3390_rs15174310 crossref_primary_10_3390_s22155667 crossref_primary_10_1109_ACCESS_2023_3292881 crossref_primary_10_1111_exsy_12930 crossref_primary_10_1109_TITS_2020_3001638 crossref_primary_10_1109_TITS_2024_3411647 crossref_primary_10_3390_s24134045 crossref_primary_10_3390_vehicles6010007 crossref_primary_10_3390_s24227376 crossref_primary_10_1109_TCSVT_2024_3398658 crossref_primary_10_1016_j_cviu_2024_103957 crossref_primary_10_3390_s24134282 crossref_primary_10_3390_math10132199 crossref_primary_10_1016_j_iatssr_2023_10_001 crossref_primary_10_1142_S0218001421500282 crossref_primary_10_3390_su142316126 crossref_primary_10_1109_TITS_2021_3099729 crossref_primary_10_3390_wevj15060255 crossref_primary_10_1007_s10489_023_05084_4 crossref_primary_10_1109_ACCESS_2023_3262466 crossref_primary_10_1088_1361_6501_ad5ea6 crossref_primary_10_1109_TITS_2022_3150715 crossref_primary_10_1109_ACCESS_2024_3512794 crossref_primary_10_3390_app122312476 crossref_primary_10_3390_jimaging10070171 crossref_primary_10_1016_j_engappai_2024_109606 crossref_primary_10_3233_JIFS_233899 crossref_primary_10_3390_electronics10192406 crossref_primary_10_4236_ojapps_2024_1412234 crossref_primary_10_3390_math12101467 crossref_primary_10_1021_acs_inorgchem_4c03151 crossref_primary_10_1109_TWC_2022_3214499 crossref_primary_10_1109_JIOT_2024_3424235 crossref_primary_10_1109_JIOT_2024_3362851 crossref_primary_10_3390_app10175763 crossref_primary_10_1007_s10489_024_05641_5 crossref_primary_10_1016_j_eswa_2023_120253 crossref_primary_10_3390_electronics13153033 |
| Cites_doi | 10.1109/ICCV.2013.286 10.1109/TAC.2008.2008327 10.1109/CVPR.2009.5206848 10.1109/CVPR.2012.6248074 10.1109/CVPR.2014.453 10.1109/AVSS.2017.8078516 10.1109/CVPR.2015.7298706 10.1109/JOE.1983.1145560 10.1109/CVPR.2016.596 10.1109/PETS-WINTER.2009.5399556 10.1109/ICCV.2015.169 10.1109/CVPR.2015.7299138 10.1109/CVPR.2011.5995311 10.1109/CVPR.2015.7299036 10.1007/978-3-030-01249-6_23 10.1007/978-3-540-74549-5_2 10.1109/ICCV.2003.1238663 10.1109/CVPR.2011.5995468 10.1109/AVSS.2017.8078518 10.1109/CVPR.2011.5995395 10.1109/ICCV.2007.4409092 10.1007/978-3-642-33709-3_25 10.1109/CVPR.2009.5206735 10.1109/CVPRW.2014.39 10.1109/CVPR.2019.00533 10.1109/ICCV.2009.5459260 10.1007/978-3-642-33718-5_35 10.1109/TPAMI.2011.21 10.1109/ICME.2017.8019461 10.1109/CVPR.2014.169 10.1109/CVPRW.2018.00141 10.1109/TPAMI.2005.223 10.1007/s11263-014-0733-5 10.1007/978-3-540-69568-4_1 10.1109/CVPR.2014.320 10.1007/s11263-019-01157-5 10.1109/CVPR.2008.4587583 10.1109/CVPR.2015.7299034 10.1109/CVPRW.2013.111 10.1109/CVPR.2018.00442 10.1109/IVS.2008.4621297 10.1109/TAC.1979.1102177 10.1109/TPAMI.2009.167 10.1109/CVPR.2016.91 10.1007/978-3-319-10602-1_26 10.1109/CVPR.2009.5206661 10.1109/TPAMI.2010.232 10.1007/s11263-013-0620-5 10.1109/CVPR.2008.4587586 10.1109/ICCV.2015.533 10.1109/CVPR.2014.81 10.1109/CVPR.2007.383180 10.1109/TPAMI.2012.159 10.1109/TPAMI.2013.185 10.1109/CVPR.2014.450 10.1109/ICCV.2015.349 10.1007/s11263-011-0437-z 10.1109/CVPR.2011.5995604 10.1007/11744047_33 10.1109/CVPR.2014.159 10.1007/978-3-319-46493-0_22 10.1109/CVPRW.2009.5206638 10.1109/ICCV.2011.6126456 10.1109/AVSS.2018.8639089 10.1109/CVPR.2012.6247893 10.1109/CVPR.2014.167 10.1109/CVPR.2011.5995667 10.1109/TPAMI.2011.155 10.1109/TPAMI.2015.2509979 10.1109/TIP.2019.2922095 10.1109/TPAMI.2015.2389824 10.1023/A:1008078328650 10.1109/AVSS.2017.8078560 10.1177/0278364916679498 10.1023/B:VISI.0000013087.49260.fb 10.1109/TPAMI.2017.2732350 10.1007/978-3-642-33783-3_8 10.1109/TPAMI.2016.2577031 10.1007/978-3-319-46493-0_32 10.1109/CVPR.2015.7298809 10.1109/CVPR.2018.00745 10.1109/AVSS.2015.7301755 10.1109/TPAMI.2014.2300479 |
| ContentType | Journal Article |
| Copyright | 2020 Elsevier Inc. |
| Copyright_xml | – notice: 2020 Elsevier Inc. |
| DBID | AAYXX CITATION |
| DOI | 10.1016/j.cviu.2020.102907 |
| DatabaseName | CrossRef |
| DatabaseTitle | CrossRef |
| DatabaseTitleList | |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Applied Sciences Engineering Computer Science |
| ExternalDocumentID | 10_1016_j_cviu_2020_102907 S1077314220300035 |
| GroupedDBID | --K --M -~X .DC .~1 0R~ 1B1 1~. 1~5 29F 4.4 457 4G. 5GY 5VS 6TJ 7-5 71M 8P~ AACTN AAEDT AAEDW AAIKC AAIKJ AAKOC AALRI AAMNW AAOAW AAQFI AAQXK AATTM AAXKI AAXUO AAYFN ABBOA ABEFU ABJNI ABMAC ABWVN ABXDB ACDAQ ACGFS ACNNM ACRPL ACZNC ADBBV ADEZE ADFGL ADJOM ADMUD ADNMO ADTZH AEBSH AECPX AEIPS AEKER AENEX AFJKZ AFTJW AGHFR AGUBO AGYEJ AHJVU AHZHX AIALX AIEXJ AIKHN AITUG AKRWK ALMA_UNASSIGNED_HOLDINGS AMRAJ ANKPU AOUOD ASPBG AVWKF AXJTR AZFZN BKOJK BLXMC BNPGV CAG COF CS3 DM4 DU5 EBS EFBJH EJD EO8 EO9 EP2 EP3 F0J F5P FDB FEDTE FGOYB FIRID FNPLU FYGXN G-Q GBLVA GBOLZ HF~ HVGLF HZ~ IHE J1W JJJVA KOM LG5 M41 MO0 N9A O-L O9- OAUVE OZT P-8 P-9 P2P PC. Q38 R2- RIG RNS ROL RPZ SDF SDG SDP SES SEW SPCBC SST SSV SSZ T5K TN5 XPP ZMT ~G- AABNK AAYWO AAYXX ABFNM ACRLP ACVFH ADCNI AEUPX AFPUW AGCQF AGQPQ AGRNS AIGII AIIUN AKBMS AKYEP APXCP BJAXD CITATION SPC |
| ID | FETCH-LOGICAL-c300t-bbe70743e1507daa3614d67c95f5e24e930e7acd1df81a48971f5dedf8f93b763 |
| IEDL.DBID | .~1 |
| ISSN | 1077-3142 |
| IngestDate | Thu Apr 24 22:52:19 EDT 2025 Tue Jul 01 04:32:07 EDT 2025 Sun Apr 06 06:54:28 EDT 2025 |
| IsDoiOpenAccess | false |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Keywords | Benchmark Evaluation protocol Object tracking Object detection |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c300t-bbe70743e1507daa3614d67c95f5e24e930e7acd1df81a48971f5dedf8f93b763 |
| ParticipantIDs | crossref_primary_10_1016_j_cviu_2020_102907 crossref_citationtrail_10_1016_j_cviu_2020_102907 elsevier_sciencedirect_doi_10_1016_j_cviu_2020_102907 |
| ProviderPackageCode | CITATION AAYXX |
| PublicationCentury | 2000 |
| PublicationDate | April 2020 2020-04-00 |
| PublicationDateYYYYMMDD | 2020-04-01 |
| PublicationDate_xml | – month: 04 year: 2020 text: April 2020 |
| PublicationDecade | 2020 |
| PublicationTitle | Computer vision and image understanding |
| PublicationYear | 2020 |
| Publisher | Elsevier Inc |
| Publisher_xml | – name: Elsevier Inc |
| References | Andriyenko, A., Schindler, K., Roth, S., 2012. Discrete-continuous optimization for multi-target tracking, In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 1926–1933. Bashir, F., Porikli, F., 2006. Performance evaluation of object detection and tracking systems. In: PETS. Ren, He, Girshick, Sun (b67) 2017; 39 Maddern, Pascoe, Linegar, Newman (b54) 2017; 36 Wu, Kan, Shan, Chen (b85) 2019; 127 Delong, Osokin, Isack, Boykov (b17) 2012; 96 Everingham, Eslami, Gool, Williams, Winn, Zisserman (b24) 2015; 111 Uijlings, van de Sande, Gevers, Smeulders (b77) 2013; 104 Rezatofighi, S.H., Milan, A., Zhang, Z., Shi, Q., Dick, A.R., Reid, I.D., 2015. Joint probabilistic data association revisited. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3047–3055. Wen, Lei, Lyu, Li, Yang (b80) 2016; 38 Wu, Z., Fuller, N.W., Theriault, D.H., Betke, M., 2014. A thermal infrared video benchmark for visual analysis. In: Workshops in Conjunction with IEEE Conference on Computer Vision and Pattern Recognition, pp. 201–208. Lyu, S., Chang, M., Du, D., Wen, L., Qi, H., Li, Y., Wei, Y., Ke, L., Hu, T., Coco, M.D., Carcagnì, P., et al., 2017. UA-DETRAC 2017: Report of AVSS2017 & IWT4S challenge on advanced traffic monitoring. In: AVSS, pp. 1–7. Geiger, Lauer, Wojek, Stiller, Urtasun (b29) 2014; 36 Yong, Meng, Zuo, Zhang (b91) 2018; 40 Viola, Jones (b78) 2004; 57 Li, Y., Huang, C., Nevatia, R., 2009. Learning to associate: Hybridboosted multi-target tracker for crowded scene. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 2953–2960. Andriluka, M., Roth, S., Schiele, B., 2008. People-tracking-by-detection and people-detection-by-tracking. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. Dalal, N., Triggs, B., 2005. Histograms of oriented gradients for human detection. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 886–893. Dalal, N., Triggs, B., Schmid, C., 2006. Human detection using oriented histograms of flow and appearance. In: Proceedings of European Conference on Computer Vision, pp. 428–441. Wen, L., Li, W., Lei, Z., Yi, D., Li, S.Z., 2014. Multiple target tracking based on undirected hierarchical relation hypergraph. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 3457–3464. Yang, B., Nevatia, R., 2012. Online learned discriminative part-based appearance models for multi-human tracking. In: Proceedings of European Conference on Computer Vision, pp. 484–498. Dicle, C., Camps, O.I., Sznaier, M., 2013. The way they move: Tracking multiple targets with similar appearance. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2304–2311. Felzenszwalb, Girshick, McAllester, Ramanan (b25) 2010; 32 Zitnick, C.L., Dollár, P., 2014. Edge boxes: Locating object proposals from edges. In: Proceedings of European Conference on Computer Vision, pp. 391–405. Russakovsky, Deng, Su, Krause, Satheesh, Ma, Huang, Karpathy, Khosla, Bernstein, Berg, Fei-Fei (b69) 2015 Deng, J., Dong, W., Socher, R., Li, L., Li, K., Li, F., 2009. ImageNet: A large-scale hierarchical image database. In: CVPR, pp. 248–255. Bae, S.H., Yoon, K., 2014. Robust online multi-object tracking based on tracklet confidence and online discriminative appearance learning. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 1218–1225. Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z., 2018. Single-Shot Refinement Neural Network for Object Detection. In: CVPR, pp. 4203–4212. Wang, L., Lu, Y., Wang, H., Zheng, Y., Ye, H., Xue, X., 2017. Evolving boxes for fast vehicle detection. In: ICME, pp. 1135–1140. Yan, J., Lei, Z., Wen, L., Li, S.Z., 2014. The fastest deformable part model for object detection. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 2497–2504. Ess, A., Leibe, B., Gool, L.J.V., 2007. Depth and appearance for mobile scene analysis. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1–8. Milan, Leal-Taixé, Reid, Roth, Schindler (b56) 2016 T: Multiple people multiple parts tracker. In: Proceedings of European Conference on Computer Vision, pp. 100–114. Wen, Wu, Wang, Chen, Li (b82) 2016 Girshick, R.B., 2015. Fast R-CNN. In: Proceedings of the IEEE International Conference on Computer Vision. Ouyang, W., Wang, X., 2012. A discriminative deep model for pedestrian detection with occlusion handling. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 3258–3265. Pirsiavash, H., Ramanan, D., Fowlkes, C.C., 2011. Globally-optimal greedy algorithms for tracking a variable number of objects. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 1201–1208. Isard, Blake (b40) 1998; 29 Zhang, L., Chu, R., Xiang, S., Liao, S., Li, S.Z., 2007. Face detection based on multi-block LBP representation. In: Proceedings of International Conference on Biometrics, pp. 11–18. Leal-Taixé, Milan, Reid, Roth, Schindler (b48) 2015 Berclaz, Fleuret, Türetken, Fua (b8) 2011; 33 Mikami, D., Otsuka, K., Yamato, J., 2009. Memory-based particle filter for face pose tracking robust under complex dynamics. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 999–1006. Izadinia, H., Saleemi, I., Li, W., Shah, M., 2012. (MP) Khan, Balch, Dellaert (b43) 2005; 27 Tang, S., Andres, B., Andriluka, M., Schiele, B., 2015. Subgraph decomposition for multi-target tracking. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 5033–5041. Du, D., Qi, Y., Yu, H., Yang, Y., Duan, K., Li, G., Zhang, W., Huang, Q., Tian, Q., 2018. The unmanned aerial vehicle benchmark: object detection and tracking. In: ECCV, pp. 375–391. Hu, J., Shen, L., Sun, G., 2018. Squeeze-and-excitation networks. In: CVPR, pp. 7132–7141. Redmon, Farhadi (b65) 2018 Redmon, J., Divvala, S.K., Girshick, R.B., Farhadi, A., 2016. You only look once: Unified, real-time object detection. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. Kim, C., Li, F., Ciptadi, A., Rehg, J.M., 2015. Multiple hypothesis tracking revisited. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 4696–4704. Wojek, C., Walk, S., Schiele, B., 2009. Multi-cue onboard pedestrian detection. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 794–801. Andriyenko, A., Schindler, K., 2011. Multi-target tracking by continuous energy minimization. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. pp. 1265–1272. Breitenstein, Reichlin, Leibe, Koller-Meier, Gool (b10) 2011; 33 Howard, Zhu, Chen, Kalenichenko, Wang, Weyand, Andreetto, Adam (b35) 2017 Yu, Xian, Chen, Liu, Liao, Madhavan, Darrell (b92) 2018 Overett, G., Petersson, L., Brewer, N., Andersson, L., Pettersson, N., 2008. A new pedestrian dataset for supervised learning. In: Proceedings of IEEE Intelligent Vehicles Symposium, pp. 373–378. Reid (b66) 1979; 24 Hwang, S., Park, J., Kim, N., Choi, Y., Kweon, I.S., 2015. Multispectral pedestrian detection: Benchmark dataset and baseline. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. Fortmann, Shalom, Scheffe (b27) 1983; 8 Yang, S., Luo, P., Loy, C.C., Tang, X., 2016. WIDER FACE: A face detection benchmark. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 5525–5533. Leal-Taixé, L., Fenzi, M., Kuznetsova, A., Rosenhahn, B., Savarese, S., 2014. Learning an image-based motion context for multiple people tracking. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 3542–3549. Shi, X., Ling, H., Hu, W., Yuan, C., Xing, J., 2014. Multi-target tracking with motion context in tensor power iteration. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 3518–3525. Amin, S., Galasso, F., 2017. Geometric proposals for faster R-CNN. In: AVSS, pp. 1–6. Huang, Li, Nevatia (b38) 2013; 35 Krizhevsky, Sutskever, Hinton (b45) 2012 Zamir, A.R., Dehghan, A., Shah, M., 2012. GMCP-Tracker: Global multi-object tracking using generalized minimum clique graphs. In: Proceedings of European Conference on Computer Vision, pp. 343–356. Lyu, S., Chang, M., Du, D., Li, W., Wei, Y., Coco, M.D., Carcagnì, P., et al., 2018. UA-DETRAC 2018: Report of AVSS2018 & IWT4S challenge on advanced traffic monitoring. In: AVSS, pp. 1–6. Milan, A., Schindler, K., Roth, S., 2013. Challenges of ground truth evaluation of multi-target tracking. In: Workshops in Conjunction with IEEE Conference on Computer Vision and Pattern Recognition, pp. 735–742. Solera, F., Calderara, S., Cucchiara, R., 2015. Towards the evaluation of reproducible robustness in tracking-by-detection. In: Proceedings of IEEE International Conference on Advanced Video and Signal-Based Surveillance. Girshick, R.B., Donahue, J., Darrell, T., Malik, J., 2014. Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 580–587. Bochinski, E., Eiselein, V., Sikora, T., 2017. High-Speed tracking-by-detection without using image information. In: AVSS, pp. 1–6. Stiefelhagen, R., Bernardin, K., Bowers, R., Garofolo, J.S., Mostefa, D., Soundararajan, P., 2006. The CLEAR 2006 evaluation. In: International Evaluation Workshop on Classification of Events, Activities and Relationships, pp. 1–44. Lampert, C.H., Blaschko, M.B., Hofmann, T., 2008. Beyond sliding windows: Object localization by efficient subwindow search. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. Yang, M., Liu, Y., Wen, L., You, Z., Li, S.Z., 2014. A probabilistic framework for multitarget tracking with mutual occlusions. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 1298–1305. Zhang, X., Zou, J., Ming, X., He, K., Sun, J., 2015. Efficient and accurate approximatio Everingham (10.1016/j.cviu.2020.102907_b24) 2015; 111 10.1016/j.cviu.2020.102907_b4 10.1016/j.cviu.2020.102907_b3 10.1016/j.cviu.2020.102907_b23 10.1016/j.cviu.2020.102907_b6 10.1016/j.cviu.2020.102907_b26 10.1016/j.cviu.2020.102907_b5 10.1016/j.cviu.2020.102907_b7 10.1016/j.cviu.2020.102907_b22 10.1016/j.cviu.2020.102907_b9 Breitenstein (10.1016/j.cviu.2020.102907_b10) 2011; 33 Wu (10.1016/j.cviu.2020.102907_b85) 2019; 127 10.1016/j.cviu.2020.102907_b39 Redmon (10.1016/j.cviu.2020.102907_b65) 2018 10.1016/j.cviu.2020.102907_b34 10.1016/j.cviu.2020.102907_b37 10.1016/j.cviu.2020.102907_b36 Fu (10.1016/j.cviu.2020.102907_b28) 2019; 28 10.1016/j.cviu.2020.102907_b31 10.1016/j.cviu.2020.102907_b30 Leven (10.1016/j.cviu.2020.102907_b49) 2009; 54 10.1016/j.cviu.2020.102907_b32 Ren (10.1016/j.cviu.2020.102907_b67) 2017; 39 Russakovsky (10.1016/j.cviu.2020.102907_b69) 2015 Yu (10.1016/j.cviu.2020.102907_b92) 2018 Fortmann (10.1016/j.cviu.2020.102907_b27) 1983; 8 Milan (10.1016/j.cviu.2020.102907_b56) 2016 10.1016/j.cviu.2020.102907_b89 10.1016/j.cviu.2020.102907_b86 10.1016/j.cviu.2020.102907_b88 10.1016/j.cviu.2020.102907_b87 10.1016/j.cviu.2020.102907_b81 10.1016/j.cviu.2020.102907_b84 Leal-Taixé (10.1016/j.cviu.2020.102907_b48) 2015 10.1016/j.cviu.2020.102907_b83 Felzenszwalb (10.1016/j.cviu.2020.102907_b25) 2010; 32 Viola (10.1016/j.cviu.2020.102907_b78) 2004; 57 Uijlings (10.1016/j.cviu.2020.102907_b77) 2013; 104 10.1016/j.cviu.2020.102907_b16 10.1016/j.cviu.2020.102907_b19 Reid (10.1016/j.cviu.2020.102907_b66) 1979; 24 10.1016/j.cviu.2020.102907_b18 10.1016/j.cviu.2020.102907_b12 Dollár (10.1016/j.cviu.2020.102907_b21) 2012; 34 10.1016/j.cviu.2020.102907_b15 10.1016/j.cviu.2020.102907_b14 10.1016/j.cviu.2020.102907_b97 Krizhevsky (10.1016/j.cviu.2020.102907_b45) 2012 10.1016/j.cviu.2020.102907_b96 10.1016/j.cviu.2020.102907_b11 10.1016/j.cviu.2020.102907_b98 Wen (10.1016/j.cviu.2020.102907_b80) 2016; 38 10.1016/j.cviu.2020.102907_b93 10.1016/j.cviu.2020.102907_b95 10.1016/j.cviu.2020.102907_b94 10.1016/j.cviu.2020.102907_b2 10.1016/j.cviu.2020.102907_b1 10.1016/j.cviu.2020.102907_b90 Huang (10.1016/j.cviu.2020.102907_b38) 2013; 35 Geiger (10.1016/j.cviu.2020.102907_b29) 2014; 36 He (10.1016/j.cviu.2020.102907_b33) 2015; 37 Khan (10.1016/j.cviu.2020.102907_b43) 2005; 27 10.1016/j.cviu.2020.102907_b68 10.1016/j.cviu.2020.102907_b64 10.1016/j.cviu.2020.102907_b63 Berclaz (10.1016/j.cviu.2020.102907_b8) 2011; 33 10.1016/j.cviu.2020.102907_b62 10.1016/j.cviu.2020.102907_b61 10.1016/j.cviu.2020.102907_b79 10.1016/j.cviu.2020.102907_b75 Howard (10.1016/j.cviu.2020.102907_b35) 2017 10.1016/j.cviu.2020.102907_b74 10.1016/j.cviu.2020.102907_b76 10.1016/j.cviu.2020.102907_b71 10.1016/j.cviu.2020.102907_b70 10.1016/j.cviu.2020.102907_b73 10.1016/j.cviu.2020.102907_b72 Delong (10.1016/j.cviu.2020.102907_b17) 2012; 96 10.1016/j.cviu.2020.102907_b46 10.1016/j.cviu.2020.102907_b47 10.1016/j.cviu.2020.102907_b42 Wen (10.1016/j.cviu.2020.102907_b82) 2016 10.1016/j.cviu.2020.102907_b41 10.1016/j.cviu.2020.102907_b44 Patino (10.1016/j.cviu.2020.102907_b60) 2017 Cai (10.1016/j.cviu.2020.102907_b13) 2015 Dollár (10.1016/j.cviu.2020.102907_b20) 2014; 36 10.1016/j.cviu.2020.102907_b57 10.1016/j.cviu.2020.102907_b59 10.1016/j.cviu.2020.102907_b58 10.1016/j.cviu.2020.102907_b53 10.1016/j.cviu.2020.102907_b52 10.1016/j.cviu.2020.102907_b55 10.1016/j.cviu.2020.102907_b51 10.1016/j.cviu.2020.102907_b50 Isard (10.1016/j.cviu.2020.102907_b40) 1998; 29 Yong (10.1016/j.cviu.2020.102907_b91) 2018; 40 Maddern (10.1016/j.cviu.2020.102907_b54) 2017; 36 |
| References_xml | – reference: Izadinia, H., Saleemi, I., Li, W., Shah, M., 2012. (MP) – reference: Lyu, S., Chang, M., Du, D., Wen, L., Qi, H., Li, Y., Wei, Y., Ke, L., Hu, T., Coco, M.D., Carcagnì, P., et al., 2017. UA-DETRAC 2017: Report of AVSS2017 & IWT4S challenge on advanced traffic monitoring. In: AVSS, pp. 1–7. – reference: Du, D., Qi, Y., Yu, H., Yang, Y., Duan, K., Li, G., Zhang, W., Huang, Q., Tian, Q., 2018. The unmanned aerial vehicle benchmark: object detection and tracking. In: ECCV, pp. 375–391. – reference: Ouyang, W., Wang, X., 2012. A discriminative deep model for pedestrian detection with occlusion handling. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 3258–3265. – reference: Girshick, R.B., Donahue, J., Darrell, T., Malik, J., 2014. Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 580–587. – reference: Deng, J., Dong, W., Socher, R., Li, L., Li, K., Li, F., 2009. ImageNet: A large-scale hierarchical image database. In: CVPR, pp. 248–255. – volume: 33 start-page: 1806 year: 2011 end-page: 1819 ident: b8 article-title: Multiple object tracking using k-shortest paths optimization publication-title: IEEE Trans. Pattern Anal. Mach. Intell. – reference: Sivic, J., Zisserman, A., 2003. Video Google: A text retrieval approach to object matching in videos. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1470–1477. – reference: Bashir, F., Porikli, F., 2006. Performance evaluation of object detection and tracking systems. In: PETS. – volume: 36 start-page: 1532 year: 2014 end-page: 1545 ident: b20 article-title: Fast feature pyramids for object detection publication-title: IEEE Trans. Pattern Anal. Mach. Intell. – reference: Ess, A., Leibe, B., Gool, L.J.V., 2007. Depth and appearance for mobile scene analysis. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1–8. – volume: 111 start-page: 98 year: 2015 end-page: 136 ident: b24 article-title: The pascal visual object classes challenge: A retrospective publication-title: Int. J. Comput. Vis. – start-page: 1106 year: 2012 end-page: 1114 ident: b45 article-title: Imagenet classification with deep convolutional neural networks publication-title: Advances in Neural Information Processing Systems – reference: Pirsiavash, H., Ramanan, D., Fowlkes, C.C., 2011. Globally-optimal greedy algorithms for tracking a variable number of objects. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 1201–1208. – volume: 28 start-page: 6077 year: 2019 end-page: 6090 ident: b28 article-title: Foreground gating and background refining network for surveillance object detection publication-title: Trans. Image Process. – volume: 37 start-page: 1904 year: 2015 end-page: 1916 ident: b33 article-title: Spatial pyramid pooling in deep convolutional networks for visual recognition publication-title: IEEE Trans. Pattern Anal. Mach. Intell. – volume: 8 start-page: 173 year: 1983 end-page: 184 ident: b27 article-title: Sonar tracking of multiple targets using joint probabilistic data association publication-title: IEEE J. Ocean. Eng. – reference: Yamaguchi, K., Berg, A.C., Ortiz, L.E., Berg, T.L., 2011. Who are you with and where are you going?. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 1345–1352. – reference: T: Multiple people multiple parts tracker. In: Proceedings of European Conference on Computer Vision, pp. 100–114. – volume: 39 start-page: 1137 year: 2017 end-page: 1149 ident: b67 article-title: Faster R-CNN: towards real-time object detection with region proposal networks publication-title: IEEE Trans. Pattern Anal. Mach. Intell. – reference: Yang, M., Liu, Y., Wen, L., You, Z., Li, S.Z., 2014. A probabilistic framework for multitarget tracking with mutual occlusions. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 1298–1305. – reference: Andriluka, M., Roth, S., Schiele, B., 2008. People-tracking-by-detection and people-detection-by-tracking. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. – reference: Yan, J., Lei, Z., Wen, L., Li, S.Z., 2014. The fastest deformable part model for object detection. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 2497–2504. – reference: Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z., 2018. Single-Shot Refinement Neural Network for Object Detection. In: CVPR, pp. 4203–4212. – reference: Simonyan, K., Zisserman, A., 2015. Very deep convolutional networks for large-scale image recognition. In: ICLR. – volume: 29 start-page: 5 year: 1998 end-page: 28 ident: b40 article-title: Condensation - conditional density propagation for visual tracking publication-title: Int. J. Comput. Vis. – volume: 104 start-page: 154 year: 2013 end-page: 171 ident: b77 article-title: Selective search for object recognition publication-title: Int. J. Comput. Vis. – reference: Zhang, L., Chu, R., Xiang, S., Liao, S., Li, S.Z., 2007. Face detection based on multi-block LBP representation. In: Proceedings of International Conference on Biometrics, pp. 11–18. – volume: 32 start-page: 1627 year: 2010 end-page: 1645 ident: b25 article-title: Object detection with discriminatively trained part-based models publication-title: IEEE Trans. Pattern Anal. Mach. Intell. – reference: Rastegari, M., Ordonez, V., Redmon, J., Farhadi, A., 2016. XNOR-Net: Imagenet classification using binary convolutional neural networks. In: Proceedings of European Conference on Computer Vision, pp. 525–542. – volume: 36 start-page: 1012 year: 2014 end-page: 1025 ident: b29 article-title: 3D traffic scene understanding from movable platforms publication-title: IEEE Trans. Pattern Anal. Mach. Intell. – reference: Li, Y., Huang, C., Nevatia, R., 2009. Learning to associate: Hybridboosted multi-target tracker for crowded scene. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 2953–2960. – reference: Dicle, C., Camps, O.I., Sznaier, M., 2013. The way they move: Tracking multiple targets with similar appearance. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2304–2311. – reference: Dalal, N., Triggs, B., Schmid, C., 2006. Human detection using oriented histograms of flow and appearance. In: Proceedings of European Conference on Computer Vision, pp. 428–441. – reference: Yang, S., Luo, P., Loy, C.C., Tang, X., 2016. WIDER FACE: A face detection benchmark. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 5525–5533. – reference: Zitnick, C.L., Dollár, P., 2014. Edge boxes: Locating object proposals from edges. In: Proceedings of European Conference on Computer Vision, pp. 391–405. – year: 2015 ident: b48 article-title: Motchallenge 2015: Towards a benchmark for multi-target tracking – year: 2017 ident: b35 article-title: Mobilenets: Efficient convolutional neural networks for mobile vision applications – volume: 36 start-page: 3 year: 2017 end-page: 15 ident: b54 article-title: 1 year, 1000 km: The oxford robot car dataset publication-title: I. J. Robotics Res. – reference: Zamir, A.R., Dehghan, A., Shah, M., 2012. GMCP-Tracker: Global multi-object tracking using generalized minimum clique graphs. In: Proceedings of European Conference on Computer Vision, pp. 343–356. – year: 2015 ident: b13 article-title: Learning complexity-aware Cascades for deep pedestrian detection publication-title: Proceedings of the IEEE International Conference on Computer Vision – reference: Dehghan, A., Assari, S.M., Shah, M., 2015. GMMCP-Tracker:globally optimal generalized maximum multi clique problem for multiple object tracking. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. – volume: 57 start-page: 137 year: 2004 end-page: 154 ident: b78 article-title: Robust real-time face detection publication-title: Int. J. Comput. Vis. – reference: Milan, A., Schindler, K., Roth, S., 2013. Challenges of ground truth evaluation of multi-target tracking. In: Workshops in Conjunction with IEEE Conference on Computer Vision and Pattern Recognition, pp. 735–742. – year: 2018 ident: b65 article-title: Yolov3: An incremental improvement – reference: Geiger, A., Lenz, P., Urtasun, R., 2012. Are we ready for Autonomous Driving? The KITTI Vision Benchmark Suite. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 3354–3361. – reference: Pellegrini, S., Ess, A., Schindler, K., Gool, L.J.V., 2009. You’ll never walk alone: Modeling social behavior for multi-target tracking. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 261–268. – reference: Andriyenko, A., Schindler, K., 2011. Multi-target tracking by continuous energy minimization. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. pp. 1265–1272. – year: 2016 ident: b56 article-title: Mot16: A benchmark for multi-object tracking – volume: 33 start-page: 1820 year: 2011 end-page: 1833 ident: b10 article-title: Online multi-person tracking-by-detection from a single, uncalibrated Camera publication-title: IEEE Trans. Pattern Anal. Mach. Intell. – reference: Hu, J., Shen, L., Sun, G., 2018. Squeeze-and-excitation networks. In: CVPR, pp. 7132–7141. – reference: Cai, Z., Fan, Q., Feris, R.S., Vasconcelos, N., 2016. A unified multi-scale deep convolutional neural network for fast object detection. In: Proceedings of European Conference on Computer Vision, pp. 354–370. – reference: Solera, F., Calderara, S., Cucchiara, R., 2015. Towards the evaluation of reproducible robustness in tracking-by-detection. In: Proceedings of IEEE International Conference on Advanced Video and Signal-Based Surveillance. – reference: Hosang, J.H., Omran, M., Benenson, R., Schiele, B., 2015. Taking a deeper look at pedestrians. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 4073–4082. – volume: 38 start-page: 1983 year: 2016 end-page: 1996 ident: b80 article-title: Exploiting hierarchical dense structures on hypergraphs for multi-object tracking publication-title: IEEE Trans. Pattern Anal. Mach. Intell. – reference: Bochinski, E., Eiselein, V., Sikora, T., 2017. High-Speed tracking-by-detection without using image information. In: AVSS, pp. 1–6. – reference: Tang, S., Andres, B., Andriluka, M., Schiele, B., 2015. Subgraph decomposition for multi-target tracking. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 5033–5041. – reference: Overett, G., Petersson, L., Brewer, N., Andersson, L., Pettersson, N., 2008. A new pedestrian dataset for supervised learning. In: Proceedings of IEEE Intelligent Vehicles Symposium, pp. 373–378. – reference: Stiefelhagen, R., Bernardin, K., Bowers, R., Garofolo, J.S., Mostefa, D., Soundararajan, P., 2006. The CLEAR 2006 evaluation. In: International Evaluation Workshop on Classification of Events, Activities and Relationships, pp. 1–44. – volume: 34 start-page: 743 year: 2012 end-page: 761 ident: b21 article-title: Pedestrian detection: An evaluation of the state of the art publication-title: IEEE Trans. Pattern Anal. Mach. Intell. – volume: 27 start-page: 1805 year: 2005 end-page: 1918 ident: b43 article-title: MCMC-based particle filtering for tracking a variable number of interacting targets publication-title: IEEE Trans. Pattern Anal. Mach. Intell. – reference: Benfold, B., Reid, I., 2011. Stable multi-target tracking in real-time surveillance video. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 3457–3464. – reference: Amin, S., Galasso, F., 2017. Geometric proposals for faster R-CNN. In: AVSS, pp. 1–6. – year: 2017 ident: b60 article-title: Pets2016 dataset website – reference: Mikami, D., Otsuka, K., Yamato, J., 2009. Memory-based particle filter for face pose tracking robust under complex dynamics. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 999–1006. – volume: 54 start-page: 370 year: 2009 end-page: 375 ident: b49 article-title: Unscented kalman filters for multiple target tracking with symmetric measurement equations publication-title: IEEE Trans. Automat. Control – reference: Dalal, N., Triggs, B., 2005. Histograms of oriented gradients for human detection. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 886–893. – reference: Hwang, S., Park, J., Kim, N., Choi, Y., Kweon, I.S., 2015. Multispectral pedestrian detection: Benchmark dataset and baseline. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. – reference: Liu, W., Liao, S., Ren, W., Hu, W., Yu, Y., 2019. High-level semantic feature detection: A new perspective for pedestrian detection. In: CVPR, pp. 5187–5196. – reference: Wu, Z., Fuller, N.W., Theriault, D.H., Betke, M., 2014. A thermal infrared video benchmark for visual analysis. In: Workshops in Conjunction with IEEE Conference on Computer Vision and Pattern Recognition, pp. 201–208. – volume: 35 start-page: 898 year: 2013 end-page: 910 ident: b38 article-title: Multiple target tracking by learning-based hierarchical association of detection responses publication-title: IEEE Trans. Pattern Anal. Mach. Intell. – reference: Wen, L., Li, W., Lei, Z., Yi, D., Li, S.Z., 2014. Multiple target tracking based on undirected hierarchical relation hypergraph. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 3457–3464. – reference: Jiang, H., Fels, S., Little, J.J., 2007. A linear programming approach for multiple object tracking. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. – reference: Zhang, L., Li, Y., Nevatia, R., 2008. Global data association for multi-object tracking using network flows. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. – reference: Andriyenko, A., Schindler, K., Roth, S., 2012. Discrete-continuous optimization for multi-target tracking, In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 1926–1933. – reference: Wang, L., Lu, Y., Wang, H., Zheng, Y., Ye, H., Xue, X., 2017. Evolving boxes for fast vehicle detection. In: ICME, pp. 1135–1140. – reference: van de Sande, K.E.A., Uijlings, J.R.R., Gevers, T., Smeulders, A.W.M., 2011. Segmentation as selective search for object recognition. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1879–1886. – reference: Zhang, X., Zou, J., Ming, X., He, K., Sun, J., 2015. Efficient and accurate approximations of nonlinear convolutional networks. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 1984–1992. – reference: Bae, S.H., Yoon, K., 2014. Robust online multi-object tracking based on tracklet confidence and online discriminative appearance learning. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 1218–1225. – reference: Shi, X., Ling, H., Hu, W., Yuan, C., Xing, J., 2014. Multi-target tracking with motion context in tensor power iteration. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 3518–3525. – start-page: 2074 year: 2016 end-page: 2082 ident: b82 article-title: Learning structured sparsity in deep neural networks publication-title: Advances in Neural Information Processing Systems – reference: Lampert, C.H., Blaschko, M.B., Hofmann, T., 2008. Beyond sliding windows: Object localization by efficient subwindow search. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. – reference: Yang, B., Nevatia, R., 2012. Online learned discriminative part-based appearance models for multi-human tracking. In: Proceedings of European Conference on Computer Vision, pp. 484–498. – year: 2018 ident: b92 article-title: BDD100K: A diverse driving video database with scalable annotation tooling – reference: Rezatofighi, S.H., Milan, A., Zhang, Z., Shi, Q., Dick, A.R., Reid, I.D., 2015. Joint probabilistic data association revisited. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3047–3055. – volume: 96 start-page: 1 year: 2012 end-page: 27 ident: b17 article-title: Fast approximate energy minimization with label costs publication-title: Int. J. Comput. Vis. – reference: Girshick, R.B., 2015. Fast R-CNN. In: Proceedings of the IEEE International Conference on Computer Vision. – reference: Wojek, C., Walk, S., Schiele, B., 2009. Multi-cue onboard pedestrian detection. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 794–801. – reference: Brendel, W., Amer, M.R., Todorovic, S., 2011. Multiobject tracking as maximum weight independent set. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 1273–1280. – start-page: 1 year: 2015 end-page: 42 ident: b69 article-title: Imagenet large scale visual recognition challenge publication-title: Int. J. Comput. Vis. – reference: Kim, C., Li, F., Ciptadi, A., Rehg, J.M., 2015. Multiple hypothesis tracking revisited. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 4696–4704. – reference: Lyu, S., Chang, M., Du, D., Li, W., Wei, Y., Coco, M.D., Carcagnì, P., et al., 2018. UA-DETRAC 2018: Report of AVSS2018 & IWT4S challenge on advanced traffic monitoring. In: AVSS, pp. 1–6. – reference: Redmon, J., Divvala, S.K., Girshick, R.B., Farhadi, A., 2016. You only look once: Unified, real-time object detection. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. – reference: Ferryman, J.M., Shahrokni, A., 2009. PETS2009: Dataset and challenge. In: Proceedings of IEEE International Conference on Advanced Video and Signal-Based Surveillance, pp. 1–6. – reference: Leal-Taixé, L., Fenzi, M., Kuznetsova, A., Rosenhahn, B., Savarese, S., 2014. Learning an image-based motion context for multiple people tracking. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 3542–3549. – volume: 24 start-page: 843 year: 1979 end-page: 854 ident: b66 article-title: An algorithm for tracking multiple targets publication-title: IEEE Trans. Automat. Control – volume: 127 start-page: 560 year: 2019 end-page: 578 ident: b85 article-title: Hierarchical attention for part-aware face detection publication-title: Int. J. Comput. Vis. – reference: Huang, X., Cheng, X., Geng, Q., Cao, B., Zhou, D., Wang, P., Lin, Y., Yang, R., 2018. The apolloscape dataset for autonomous driving. In: CVPRWorkshops, pp. 954–960. – volume: 40 start-page: 1726 year: 2018 end-page: 1740 ident: b91 article-title: Robust online matrix factorization for dynamic background subtraction publication-title: IEEE Trans. Pattern Anal. Mach. Intell. – ident: 10.1016/j.cviu.2020.102907_b19 doi: 10.1109/ICCV.2013.286 – volume: 54 start-page: 370 issue: 2 year: 2009 ident: 10.1016/j.cviu.2020.102907_b49 article-title: Unscented kalman filters for multiple target tracking with symmetric measurement equations publication-title: IEEE Trans. Automat. Control doi: 10.1109/TAC.2008.2008327 – ident: 10.1016/j.cviu.2020.102907_b18 doi: 10.1109/CVPR.2009.5206848 – ident: 10.1016/j.cviu.2020.102907_b30 doi: 10.1109/CVPR.2012.6248074 – ident: 10.1016/j.cviu.2020.102907_b47 doi: 10.1109/CVPR.2014.453 – ident: 10.1016/j.cviu.2020.102907_b9 doi: 10.1109/AVSS.2017.8078516 – ident: 10.1016/j.cviu.2020.102907_b39 doi: 10.1109/CVPR.2015.7298706 – volume: 8 start-page: 173 issue: 3 year: 1983 ident: 10.1016/j.cviu.2020.102907_b27 article-title: Sonar tracking of multiple targets using joint probabilistic data association publication-title: IEEE J. Ocean. Eng. doi: 10.1109/JOE.1983.1145560 – year: 2017 ident: 10.1016/j.cviu.2020.102907_b60 – ident: 10.1016/j.cviu.2020.102907_b89 doi: 10.1109/CVPR.2016.596 – ident: 10.1016/j.cviu.2020.102907_b26 doi: 10.1109/PETS-WINTER.2009.5399556 – ident: 10.1016/j.cviu.2020.102907_b31 doi: 10.1109/ICCV.2015.169 – ident: 10.1016/j.cviu.2020.102907_b76 doi: 10.1109/CVPR.2015.7299138 – ident: 10.1016/j.cviu.2020.102907_b6 – ident: 10.1016/j.cviu.2020.102907_b3 doi: 10.1109/CVPR.2011.5995311 – ident: 10.1016/j.cviu.2020.102907_b16 doi: 10.1109/CVPR.2015.7299036 – ident: 10.1016/j.cviu.2020.102907_b22 doi: 10.1007/978-3-030-01249-6_23 – ident: 10.1016/j.cviu.2020.102907_b94 doi: 10.1007/978-3-540-74549-5_2 – ident: 10.1016/j.cviu.2020.102907_b73 doi: 10.1109/ICCV.2003.1238663 – ident: 10.1016/j.cviu.2020.102907_b86 doi: 10.1109/CVPR.2011.5995468 – ident: 10.1016/j.cviu.2020.102907_b1 doi: 10.1109/AVSS.2017.8078518 – ident: 10.1016/j.cviu.2020.102907_b11 doi: 10.1109/CVPR.2011.5995395 – ident: 10.1016/j.cviu.2020.102907_b23 doi: 10.1109/ICCV.2007.4409092 – ident: 10.1016/j.cviu.2020.102907_b93 doi: 10.1007/978-3-642-33709-3_25 – ident: 10.1016/j.cviu.2020.102907_b50 doi: 10.1109/CVPR.2009.5206735 – ident: 10.1016/j.cviu.2020.102907_b84 doi: 10.1109/CVPRW.2014.39 – ident: 10.1016/j.cviu.2020.102907_b51 doi: 10.1109/CVPR.2019.00533 – ident: 10.1016/j.cviu.2020.102907_b61 doi: 10.1109/ICCV.2009.5459260 – ident: 10.1016/j.cviu.2020.102907_b58 – ident: 10.1016/j.cviu.2020.102907_b90 doi: 10.1007/978-3-642-33718-5_35 – volume: 33 start-page: 1806 issue: 9 year: 2011 ident: 10.1016/j.cviu.2020.102907_b8 article-title: Multiple object tracking using k-shortest paths optimization publication-title: IEEE Trans. Pattern Anal. Mach. Intell. doi: 10.1109/TPAMI.2011.21 – ident: 10.1016/j.cviu.2020.102907_b79 doi: 10.1109/ICME.2017.8019461 – year: 2015 ident: 10.1016/j.cviu.2020.102907_b13 article-title: Learning complexity-aware Cascades for deep pedestrian detection – ident: 10.1016/j.cviu.2020.102907_b88 doi: 10.1109/CVPR.2014.169 – year: 2018 ident: 10.1016/j.cviu.2020.102907_b65 – ident: 10.1016/j.cviu.2020.102907_b37 doi: 10.1109/CVPRW.2018.00141 – volume: 27 start-page: 1805 issue: 11 year: 2005 ident: 10.1016/j.cviu.2020.102907_b43 article-title: MCMC-based particle filtering for tracking a variable number of interacting targets publication-title: IEEE Trans. Pattern Anal. Mach. Intell. doi: 10.1109/TPAMI.2005.223 – volume: 111 start-page: 98 issue: 1 year: 2015 ident: 10.1016/j.cviu.2020.102907_b24 article-title: The pascal visual object classes challenge: A retrospective publication-title: Int. J. Comput. Vis. doi: 10.1007/s11263-014-0733-5 – ident: 10.1016/j.cviu.2020.102907_b75 doi: 10.1007/978-3-540-69568-4_1 – ident: 10.1016/j.cviu.2020.102907_b87 doi: 10.1109/CVPR.2014.320 – volume: 127 start-page: 560 issue: 6–7 year: 2019 ident: 10.1016/j.cviu.2020.102907_b85 article-title: Hierarchical attention for part-aware face detection publication-title: Int. J. Comput. Vis. doi: 10.1007/s11263-019-01157-5 – ident: 10.1016/j.cviu.2020.102907_b2 doi: 10.1109/CVPR.2008.4587583 – ident: 10.1016/j.cviu.2020.102907_b34 doi: 10.1109/CVPR.2015.7299034 – ident: 10.1016/j.cviu.2020.102907_b57 doi: 10.1109/CVPRW.2013.111 – ident: 10.1016/j.cviu.2020.102907_b72 – year: 2016 ident: 10.1016/j.cviu.2020.102907_b56 – ident: 10.1016/j.cviu.2020.102907_b96 doi: 10.1109/CVPR.2018.00442 – ident: 10.1016/j.cviu.2020.102907_b59 doi: 10.1109/IVS.2008.4621297 – volume: 24 start-page: 843 year: 1979 ident: 10.1016/j.cviu.2020.102907_b66 article-title: An algorithm for tracking multiple targets publication-title: IEEE Trans. Automat. Control doi: 10.1109/TAC.1979.1102177 – volume: 32 start-page: 1627 issue: 9 year: 2010 ident: 10.1016/j.cviu.2020.102907_b25 article-title: Object detection with discriminatively trained part-based models publication-title: IEEE Trans. Pattern Anal. Mach. Intell. doi: 10.1109/TPAMI.2009.167 – ident: 10.1016/j.cviu.2020.102907_b64 doi: 10.1109/CVPR.2016.91 – ident: 10.1016/j.cviu.2020.102907_b98 doi: 10.1007/978-3-319-10602-1_26 – start-page: 1106 year: 2012 ident: 10.1016/j.cviu.2020.102907_b45 article-title: Imagenet classification with deep convolutional neural networks – start-page: 1 year: 2015 ident: 10.1016/j.cviu.2020.102907_b69 article-title: Imagenet large scale visual recognition challenge publication-title: Int. J. Comput. Vis. – ident: 10.1016/j.cviu.2020.102907_b55 doi: 10.1109/CVPR.2009.5206661 – volume: 33 start-page: 1820 issue: 9 year: 2011 ident: 10.1016/j.cviu.2020.102907_b10 article-title: Online multi-person tracking-by-detection from a single, uncalibrated Camera publication-title: IEEE Trans. Pattern Anal. Mach. Intell. doi: 10.1109/TPAMI.2010.232 – volume: 104 start-page: 154 issue: 2 year: 2013 ident: 10.1016/j.cviu.2020.102907_b77 article-title: Selective search for object recognition publication-title: Int. J. Comput. Vis. doi: 10.1007/s11263-013-0620-5 – ident: 10.1016/j.cviu.2020.102907_b46 doi: 10.1109/CVPR.2008.4587586 – year: 2018 ident: 10.1016/j.cviu.2020.102907_b92 – ident: 10.1016/j.cviu.2020.102907_b44 doi: 10.1109/ICCV.2015.533 – ident: 10.1016/j.cviu.2020.102907_b32 doi: 10.1109/CVPR.2014.81 – ident: 10.1016/j.cviu.2020.102907_b42 doi: 10.1109/CVPR.2007.383180 – volume: 35 start-page: 898 issue: 4 year: 2013 ident: 10.1016/j.cviu.2020.102907_b38 article-title: Multiple target tracking by learning-based hierarchical association of detection responses publication-title: IEEE Trans. Pattern Anal. Mach. Intell. doi: 10.1109/TPAMI.2012.159 – volume: 36 start-page: 1012 issue: 5 year: 2014 ident: 10.1016/j.cviu.2020.102907_b29 article-title: 3D traffic scene understanding from movable platforms publication-title: IEEE Trans. Pattern Anal. Mach. Intell. doi: 10.1109/TPAMI.2013.185 – ident: 10.1016/j.cviu.2020.102907_b95 – ident: 10.1016/j.cviu.2020.102907_b71 doi: 10.1109/CVPR.2014.450 – year: 2017 ident: 10.1016/j.cviu.2020.102907_b35 – ident: 10.1016/j.cviu.2020.102907_b68 doi: 10.1109/ICCV.2015.349 – volume: 96 start-page: 1 issue: 1 year: 2012 ident: 10.1016/j.cviu.2020.102907_b17 article-title: Fast approximate energy minimization with label costs publication-title: Int. J. Comput. Vis. doi: 10.1007/s11263-011-0437-z – ident: 10.1016/j.cviu.2020.102907_b62 doi: 10.1109/CVPR.2011.5995604 – ident: 10.1016/j.cviu.2020.102907_b15 doi: 10.1007/11744047_33 – ident: 10.1016/j.cviu.2020.102907_b5 doi: 10.1109/CVPR.2014.159 – ident: 10.1016/j.cviu.2020.102907_b12 doi: 10.1007/978-3-319-46493-0_22 – ident: 10.1016/j.cviu.2020.102907_b83 doi: 10.1109/CVPRW.2009.5206638 – ident: 10.1016/j.cviu.2020.102907_b70 doi: 10.1109/ICCV.2011.6126456 – ident: 10.1016/j.cviu.2020.102907_b52 doi: 10.1109/AVSS.2018.8639089 – ident: 10.1016/j.cviu.2020.102907_b4 doi: 10.1109/CVPR.2012.6247893 – ident: 10.1016/j.cviu.2020.102907_b81 doi: 10.1109/CVPR.2014.167 – ident: 10.1016/j.cviu.2020.102907_b7 doi: 10.1109/CVPR.2011.5995667 – volume: 34 start-page: 743 issue: 4 year: 2012 ident: 10.1016/j.cviu.2020.102907_b21 article-title: Pedestrian detection: An evaluation of the state of the art publication-title: IEEE Trans. Pattern Anal. Mach. Intell. doi: 10.1109/TPAMI.2011.155 – volume: 38 start-page: 1983 issue: 10 year: 2016 ident: 10.1016/j.cviu.2020.102907_b80 article-title: Exploiting hierarchical dense structures on hypergraphs for multi-object tracking publication-title: IEEE Trans. Pattern Anal. Mach. Intell. doi: 10.1109/TPAMI.2015.2509979 – volume: 28 start-page: 6077 issue: 12 year: 2019 ident: 10.1016/j.cviu.2020.102907_b28 article-title: Foreground gating and background refining network for surveillance object detection publication-title: Trans. Image Process. doi: 10.1109/TIP.2019.2922095 – volume: 37 start-page: 1904 issue: 9 year: 2015 ident: 10.1016/j.cviu.2020.102907_b33 article-title: Spatial pyramid pooling in deep convolutional networks for visual recognition publication-title: IEEE Trans. Pattern Anal. Mach. Intell. doi: 10.1109/TPAMI.2015.2389824 – volume: 29 start-page: 5 issue: 1 year: 1998 ident: 10.1016/j.cviu.2020.102907_b40 article-title: Condensation - conditional density propagation for visual tracking publication-title: Int. J. Comput. Vis. doi: 10.1023/A:1008078328650 – ident: 10.1016/j.cviu.2020.102907_b53 doi: 10.1109/AVSS.2017.8078560 – volume: 36 start-page: 3 issue: 1 year: 2017 ident: 10.1016/j.cviu.2020.102907_b54 article-title: 1 year, 1000 km: The oxford robot car dataset publication-title: I. J. Robotics Res. doi: 10.1177/0278364916679498 – volume: 57 start-page: 137 issue: 2 year: 2004 ident: 10.1016/j.cviu.2020.102907_b78 article-title: Robust real-time face detection publication-title: Int. J. Comput. Vis. doi: 10.1023/B:VISI.0000013087.49260.fb – volume: 40 start-page: 1726 issue: 7 year: 2018 ident: 10.1016/j.cviu.2020.102907_b91 article-title: Robust online matrix factorization for dynamic background subtraction publication-title: IEEE Trans. Pattern Anal. Mach. Intell. doi: 10.1109/TPAMI.2017.2732350 – ident: 10.1016/j.cviu.2020.102907_b41 doi: 10.1007/978-3-642-33783-3_8 – volume: 39 start-page: 1137 issue: 6 year: 2017 ident: 10.1016/j.cviu.2020.102907_b67 article-title: Faster R-CNN: towards real-time object detection with region proposal networks publication-title: IEEE Trans. Pattern Anal. Mach. Intell. doi: 10.1109/TPAMI.2016.2577031 – ident: 10.1016/j.cviu.2020.102907_b14 – ident: 10.1016/j.cviu.2020.102907_b63 doi: 10.1007/978-3-319-46493-0_32 – ident: 10.1016/j.cviu.2020.102907_b97 doi: 10.1109/CVPR.2015.7298809 – ident: 10.1016/j.cviu.2020.102907_b36 doi: 10.1109/CVPR.2018.00745 – start-page: 2074 year: 2016 ident: 10.1016/j.cviu.2020.102907_b82 article-title: Learning structured sparsity in deep neural networks – ident: 10.1016/j.cviu.2020.102907_b74 doi: 10.1109/AVSS.2015.7301755 – year: 2015 ident: 10.1016/j.cviu.2020.102907_b48 – volume: 36 start-page: 1532 issue: 8 year: 2014 ident: 10.1016/j.cviu.2020.102907_b20 article-title: Fast feature pyramids for object detection publication-title: IEEE Trans. Pattern Anal. Mach. Intell. doi: 10.1109/TPAMI.2014.2300479 |
| SSID | ssj0011491 |
| Score | 2.6943223 |
| Snippet | Effective multi-object tracking (MOT) methods have been developed in recent years for a wide range of applications including visual surveillance and behavior... |
| SourceID | crossref elsevier |
| SourceType | Enrichment Source Index Database Publisher |
| StartPage | 102907 |
| SubjectTerms | Benchmark Evaluation protocol Object detection Object tracking |
| Title | UA-DETRAC: A new benchmark and protocol for multi-object detection and tracking |
| URI | https://dx.doi.org/10.1016/j.cviu.2020.102907 |
| Volume | 193 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV1LS8NAEB6KXvTgoyrWR9mDN1nbvBNvobZUxQraQm8h2d1gfaSlph797c4km1JBehBy2WU2hMlk5tvNNzMAF5YhhXKlySnNktsydXhgmgk5w0ApugxKTn4YuP2RfTd2xjXoVLkwRKvUvr_06YW31jMtrc3WbDJpPePGxbPoCAPtlH6IUQa77ZGVX30vaR4I94uueSTMSVonzpQcL_E1WeAe0SwqGATUUvav4LQScHp7sKORIgvLh9mHmsrqsKtRI9Pf5CdOVY0Zqrk6bK9UGTyAx1HIb7rDp7BzzUKGMJolKPbyEc_fWJxJRqUapmgPDPErKwiGfJrQ8QyTKi-YWlkhl89jQQfrhzDqdYedPtd9FLhAzeQ8SZRHSEER-JNxbGFIlq4nAid1lGmrwGorLxbSkKlvxLYfeEbqSIWjNLASdEBHsJFNM3UMTLlty29LjOrSsvFefizQCqhbmRAuijfAqBQYCV1knHpdvEcVm-w1IqVHpPSoVHoDLpdrZmWJjbXSTvVeol-GEmEMWLPu5J_rTmGLRiVZ5ww28vlCnSMOyZNmYWhN2Axv7_uDH8Gs2wo |
| linkProvider | Elsevier |
| linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV1LT8JAEJ4gHNSDD9SIzz14Mw30Tb01CAF5mCgk3pp2dxvxUQgWf78z7ZZgYjiY9NLtbLOZTmdmZ7-ZAbgxdcGlIwyN0iw1S8S25hlGRMrQk5IunZKThyOnO7EeXuyXErSKXBiCVSrdn-v0TFurkbriZn0-ndafcePimhTCQDmlA7EtqFB1KrsMFb_X745Whwm4CdBz6CGF5CxD5c7kMC_-PV3iNtHIihh41FX2L_u0ZnM6B7CnnEXm5-s5hJJMqrCvHEemfssvHCp6MxRjVdhdKzR4BI8TX7tvj5_81h3zGXrSLEKy189w8c7CRDCq1jBDkWDowrIMY6jNIorQMCHTDKyVZHTpIuQUWz-GSac9bnU11UpB48icVIsi6ZKzIMn_E2FoolUWjss9O7alYUnPbEg35EIXcVMPrabn6rEtJN7FnhmhDjqBcjJL5Ckw6TTMZkOgYRemhe9qhhwFgRqWce4geQ30goEBV3XGqd3FR1AAyt4CYnpATA9yptfgdjVnnlfZ2EhtF98l-CUrAZqBDfPO_jnvGra74-EgGPRG_XPYoSc5ducCyuliKS_RLUmjKyV2P1Nt3bs |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=UA-DETRAC%3A+A+new+benchmark+and+protocol+for+multi-object+detection+and+tracking&rft.jtitle=Computer+vision+and+image+understanding&rft.au=Wen%2C+Longyin&rft.au=Du%2C+Dawei&rft.au=Cai%2C+Zhaowei&rft.au=Lei%2C+Zhen&rft.date=2020-04-01&rft.issn=1077-3142&rft.volume=193&rft.spage=102907&rft_id=info:doi/10.1016%2Fj.cviu.2020.102907&rft.externalDBID=n%2Fa&rft.externalDocID=10_1016_j_cviu_2020_102907 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1077-3142&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1077-3142&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1077-3142&client=summon |