A Survey of Multiple Pedestrian Tracking Based on Tracking-by-Detection Framework
Multiple pedestrian tracking (MPT) has gained significant attention due to its huge potential in a commercial application. It aims to predict multiple pedestrian trajectories and maintain their identities, given a video sequence. In the past decade, due to the advancement in pedestrian detection alg...
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| Published in | IEEE transactions on circuits and systems for video technology Vol. 31; no. 5; pp. 1819 - 1833 |
|---|---|
| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
New York
IEEE
01.05.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subjects | |
| Online Access | Get full text |
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| Abstract | Multiple pedestrian tracking (MPT) has gained significant attention due to its huge potential in a commercial application. It aims to predict multiple pedestrian trajectories and maintain their identities, given a video sequence. In the past decade, due to the advancement in pedestrian detection algorithms, Tracking-by-Detection (TBD) based algorithms have achieved tremendous successes. TBD has become the most popular MPT framework, and it has been actively studied in the past decade. In this paper, we give a comprehensive survey of recent advances in TBD-based MPT algorithms. We systematically analyze the existing TBD-based algorithms and organize the survey into four major parts. At first, this survey draws a timeline to introduce the milestones of TBD-based works which briefly reviews the development of the existing TBD-based methods. Second, the main procedures of the TBD framework are summarized, and each stage in the procedure is described in detail. Afterward, this survey analyzes the performance of existing TBD-based algorithms on MOT challenge datasets and discusses the factors that affect tracking performance. Finally, open issues and future directions in the TBD framework are discussed. |
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| AbstractList | Multiple pedestrian tracking (MPT) has gained significant attention due to its huge potential in a commercial application. It aims to predict multiple pedestrian trajectories and maintain their identities, given a video sequence. In the past decade, due to the advancement in pedestrian detection algorithms, Tracking-by-Detection (TBD) based algorithms have achieved tremendous successes. TBD has become the most popular MPT framework, and it has been actively studied in the past decade. In this paper, we give a comprehensive survey of recent advances in TBD-based MPT algorithms. We systematically analyze the existing TBD-based algorithms and organize the survey into four major parts. At first, this survey draws a timeline to introduce the milestones of TBD-based works which briefly reviews the development of the existing TBD-based methods. Second, the main procedures of the TBD framework are summarized, and each stage in the procedure is described in detail. Afterward, this survey analyzes the performance of existing TBD-based algorithms on MOT challenge datasets and discusses the factors that affect tracking performance. Finally, open issues and future directions in the TBD framework are discussed. |
| Author | Chen, Jun Chao, Liang Ruan, Weijian Sun, Zhihong Mukherjee, Mithun |
| Author_xml | – sequence: 1 givenname: Zhihong orcidid: 0000-0002-6202-5356 surname: Sun fullname: Sun, Zhihong email: zhihong.sun@whu.edu.cn organization: National Engineering Research Center for Multimedia Software, School of Computer Science, Wuhan University, Wuhan, China – sequence: 2 givenname: Jun orcidid: 0000-0003-1376-0167 surname: Chen fullname: Chen, Jun email: chenj.whu@gmail.com organization: National Engineering Research Center for Multimedia Software, School of Computer Science, Wuhan University, Wuhan, China – sequence: 3 givenname: Liang orcidid: 0000-0002-8287-8655 surname: Chao fullname: Chao, Liang email: cliang@whu.edu.cn organization: National Engineering Research Center for Multimedia Software, School of Computer Science, Wuhan University, Wuhan, China – sequence: 4 givenname: Weijian orcidid: 0000-0003-3710-8739 surname: Ruan fullname: Ruan, Weijian email: rweij@whu.edu.cn organization: National Engineering Research Center for Multimedia Software, School of Computer Science, Wuhan University, Wuhan, China – sequence: 5 givenname: Mithun orcidid: 0000-0002-6605-180X surname: Mukherjee fullname: Mukherjee, Mithun email: m.mukherjee@ieee.org organization: School of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing, China |
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| Cites_doi | 10.1007/978-3-642-33783-3_8 10.1109/ICCV.2015.534 10.1109/CVPR.2005.177 10.1109/ICCV.2019.00627 10.1109/CVPR.2011.5995395 10.1109/CVPRW.2019.00105 10.1109/ICSMC.2004.1400815 10.1109/CVPRW.2018.00192 10.1109/AVSS.2017.8078516 10.1109/CVPR.2015.7299138 10.1007/978-3-319-48881-3_8 10.1109/CVPR.2019.00511 10.1109/TMM.2018.2872897 10.1109/TCSVT.2020.2975842 10.1109/CVPR.2013.472 10.1007/978-3-642-33718-5_35 10.1109/TPAMI.2018.2876253 10.1049/iet-cvi.2018.5598 10.1109/ICME.2018.8486454 10.1109/ICCVW.2019.00025 10.1109/TCSVT.2018.2881123 10.1109/ICCV.2017.518 10.1109/TPAMI.2008.57 10.1109/ICRAS49812.2020.9135065 10.1109/CVPR.2017.292 10.1109/ICCVW.2019.00022 10.1109/CVPR.2016.461 10.1049/iet-cvi.2016.0178 10.1109/ICCV.2009.5459301 10.1109/CVPR.2015.7299193 10.1109/CVPR.2008.4587577 10.1007/978-3-319-48881-3_2 10.1109/TIP.2011.2159228 10.1109/ICIP.2014.7025168 10.1109/CVPRW.2017.266 10.1109/CVPR.2018.00644 10.1007/978-3-319-48881-3_7 10.1109/ICCV.2019.00409 10.1007/978-3-642-33765-9_16 10.1109/TCSVT.2018.2856540 10.1109/CVPRW.2016.161 10.1007/978-3-030-01237-3_13 10.1145/3240508.3240548 10.1109/ICCV.2019.00103 10.1109/ACCESS.2018.2881019 10.1109/ICIP.2017.8296360 10.1109/ICCV.2017.593 10.4218/etrij.15.0114.0629 10.1109/CVPR.2013.240 10.1109/TPAMI.2017.2691768 10.1109/WACV.2018.00057 10.1109/CVPR.2019.00813 10.1109/ICCV.2017.278 10.1109/ICME.2018.8486597 10.1109/CVPR.2008.4587584 10.1109/LRA.2020.2974392 10.1109/TIP.2017.2745103 10.2528/PIERB15010503 10.1109/TPAMI.2017.2691769 10.1109/WACV.2018.00128 10.1109/TPAMI.2016.2551245 10.2991/ijcis.2019.125905651 10.1109/ICCV.2017.322 10.1109/CVPR.2017.394 10.1109/ACCESS.2019.2903121 10.1007/978-81-322-1665-0_52 10.1109/ICIP.2016.7533003 10.1109/CVPR.2011.5995604 10.1016/j.neucom.2019.11.023 10.1016/j.patcog.2019.04.018 10.1109/CVPR.2016.234 10.1109/CVPR.2018.00232 10.1109/TPAMI.2015.2505309 10.1109/ICIP.2018.8451472 10.1109/LSP.2019.2940922 10.1007/s11042-018-5781-3 10.1016/j.cviu.2016.05.003 10.1145/3343031.3350984 10.1109/TMM.2016.2605058 10.1109/CVPR.2012.6247893 10.1109/CVPR.2013.241 10.1109/CVPRW.2018.00195 10.1007/s13592-011-0060-6 10.1109/ICCV.2019.00245 10.1109/CVPRW.2018.00169 10.1109/TPAMI.2012.42 10.1109/WACV.2019.00023 10.1145/3343031.3350853 10.1109/CVPR.2017.403 10.1109/GlobalSIP.2016.7905816 10.1109/CVPR.2015.7299178 10.1109/TIP.2018.2843129 10.1109/WMVC.2008.4544058 10.1007/978-3-030-01219-9_36 10.1109/TPAMI.2009.167 10.1109/CVPR.2017.495 10.1109/IVS.2019.8813779 10.1016/j.neucom.2015.07.028 10.1109/CVPR.2015.7299036 10.1109/ICCV.2015.533 10.1109/TCSVT.2018.2882192 10.1109/CVPR.2014.161 10.1109/TPAMI.2013.221 10.1007/s11263-013-0666-4 10.1109/CVPR.2015.7298718 10.1109/TPAMI.2019.2929520 10.1016/j.cviu.2016.12.003 10.1109/CVPR.2010.5540102 10.1155/2008/246309 10.1109/ROBIO.2015.7419004 10.1109/ICPCSI.2017.8392001 10.1109/ICCV.2017.41 10.1109/TPAMI.2013.103 10.1109/CVPR.2011.5995311 10.1109/CVPR.2017.206 10.1016/j.neucom.2018.02.068 10.1109/CVPR.2017.142 10.1109/CVPR.2011.5995587 10.1007/978-3-642-33715-4_43 10.1109/ICCV.2009.5459278 10.1109/ICIP.2017.8296962 10.1109/TCSVT.2018.2825679 |
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| References | ref56 ref59 ref58 ref53 ref52 ref55 ref54 dendorfer (ref70) 2019 wang (ref93) 2014; 9273 leal-taixé (ref66) 2015 ref51 ref46 ref45 ref48 yang (ref65) 2014 agarwal (ref142) 2017 ref44 ref43 milan (ref57) 2017 ma (ref114) 2018 ref49 ref7 ref9 ref3 thoreau (ref41) 2018 ref6 xing (ref47) 2009 ref5 ref100 ref101 feng (ref50) 2019 ref40 ren (ref68) 2015 ref35 ref34 ref37 ref36 ref31 ref30 ref33 ref146 ref147 gautam (ref60) 2020 ref39 ref38 ref24 ref23 ref26 shen (ref32) 2018 ref25 ref20 ref22 ref21 ref28 ref29 ref13 ref12 ref128 ref15 ref129 ref14 ref126 ref97 ref127 ref96 ref124 ref99 ref11 ref125 ref98 ref10 xu (ref117) 2019 ref17 ref16 tian (ref134) 2019; 20 ref19 ref133 ref92 ref131 ref95 ref132 ref94 ref130 ref91 ref90 xiang (ref112) 2019 ref89 ref137 ref86 ref138 ref85 ref135 sadeghi (ref67) 2014 hu (ref144) 2020 ref88 ref136 ref87 yoon (ref8) 2019 hu (ref18) 2012; 34 ref82 ref145 ref81 ref143 ref83 ref140 ref141 ref80 ref79 ref108 ref78 ref109 ref106 ref107 keuper (ref84) 2016 ref75 ref104 ref74 ref105 ref77 ref102 ref76 ref103 ref2 lin (ref27) 2014 ref71 ref111 ref73 ref110 song (ref72) 2019 ref119 ref69 ref118 ref64 ref115 ref63 ref116 ref113 zamir (ref42) 2012 redmon (ref139) 2018 luo (ref1) 2014 ref122 ref123 ref62 ref120 ref61 ref121 zhou (ref4) 2018 |
| References_xml | – ident: ref94 doi: 10.1007/978-3-642-33783-3_8 – ident: ref89 doi: 10.1109/ICCV.2015.534 – start-page: 1200 year: 2009 ident: ref47 article-title: Multi-object tracking through occlusions by local tracklets filtering and global tracklets association with detection responses publication-title: Proc IEEE Conf Comput Vis Pattern Recognit – ident: ref62 doi: 10.1109/CVPR.2005.177 – ident: ref49 doi: 10.1109/ICCV.2019.00627 – ident: ref34 doi: 10.1109/CVPR.2011.5995395 – ident: ref28 doi: 10.1109/CVPRW.2019.00105 – ident: ref64 doi: 10.1109/ICSMC.2004.1400815 – year: 2019 ident: ref112 article-title: End-to-end learning deep CRF models for multi-object tracking publication-title: arXiv 1907 12176 – ident: ref136 doi: 10.1109/CVPRW.2018.00192 – ident: ref106 doi: 10.1109/AVSS.2017.8078516 – ident: ref44 doi: 10.1109/CVPR.2015.7299138 – ident: ref45 doi: 10.1007/978-3-319-48881-3_8 – ident: ref26 doi: 10.1109/CVPR.2019.00511 – ident: ref12 doi: 10.1109/TMM.2018.2872897 – ident: ref146 doi: 10.1109/TCSVT.2020.2975842 – volume: 9273 start-page: 1 year: 2014 ident: ref93 article-title: Coupled data association and L1 minimization for multiple object tracking under occlusion publication-title: Proc SPIE – ident: ref36 doi: 10.1109/CVPR.2013.472 – ident: ref14 doi: 10.1007/978-3-642-33718-5_35 – ident: ref83 doi: 10.1109/TPAMI.2018.2876253 – ident: ref5 doi: 10.1049/iet-cvi.2018.5598 – ident: ref131 doi: 10.1109/ICME.2018.8486454 – ident: ref101 doi: 10.1109/ICCVW.2019.00025 – ident: ref113 doi: 10.1109/TCSVT.2018.2881123 – ident: ref125 doi: 10.1109/ICCV.2017.518 – ident: ref127 doi: 10.1109/TPAMI.2008.57 – ident: ref61 doi: 10.1109/ICRAS49812.2020.9135065 – ident: ref25 doi: 10.1109/CVPR.2017.292 – ident: ref71 doi: 10.1109/ICCVW.2019.00022 – ident: ref102 doi: 10.1109/CVPR.2016.461 – start-page: 612 year: 2018 ident: ref114 article-title: Customized multi-person tracker publication-title: Vision Computer – ident: ref3 doi: 10.1049/iet-cvi.2016.0178 – ident: ref96 doi: 10.1109/ICCV.2009.5459301 – ident: ref24 doi: 10.1109/CVPR.2015.7299193 – ident: ref33 doi: 10.1109/CVPR.2008.4587577 – ident: ref129 doi: 10.1007/978-3-319-48881-3_2 – year: 2019 ident: ref50 article-title: Multi-object tracking with multiple cues and switcher-aware classification publication-title: arXiv 1901 06129 – ident: ref51 doi: 10.1109/TIP.2011.2159228 – ident: ref92 doi: 10.1109/ICIP.2014.7025168 – ident: ref137 doi: 10.1109/CVPRW.2017.266 – ident: ref140 doi: 10.1109/CVPR.2018.00644 – ident: ref76 doi: 10.1007/978-3-319-48881-3_7 – ident: ref103 doi: 10.1109/ICCV.2019.00409 – ident: ref23 doi: 10.1007/978-3-642-33765-9_16 – ident: ref95 doi: 10.1109/TCSVT.2018.2856540 – ident: ref75 doi: 10.1109/CVPRW.2016.161 – ident: ref85 doi: 10.1007/978-3-030-01237-3_13 – ident: ref104 doi: 10.1145/3240508.3240548 – ident: ref100 doi: 10.1109/ICCV.2019.00103 – ident: ref133 doi: 10.1109/ACCESS.2018.2881019 – ident: ref122 doi: 10.1109/ICIP.2017.8296360 – year: 2018 ident: ref32 article-title: Tracklet association tracker: An end-to-end learning-based association approach for multi-object tracking publication-title: arXiv 1808 01562 – ident: ref115 doi: 10.1109/ICCV.2017.593 – ident: ref77 doi: 10.4218/etrij.15.0114.0629 – ident: ref17 doi: 10.1109/CVPR.2013.240 – ident: ref10 doi: 10.1109/TPAMI.2017.2691768 – ident: ref86 doi: 10.1109/WACV.2018.00057 – ident: ref53 doi: 10.1109/CVPR.2019.00813 – year: 2017 ident: ref142 article-title: Real-time multiple object tracking (MOT) for autonomous navigation – year: 2018 ident: ref41 article-title: Deep similarity metric learning for real-time pedestrian tracking publication-title: arXiv 1806 07592 – ident: ref119 doi: 10.1109/ICCV.2017.278 – ident: ref56 doi: 10.1109/ICME.2018.8486597 – start-page: 65 year: 2014 ident: ref67 article-title: 30 Hz object detection with DPM V5 publication-title: Proc Eur Conf Comput Vis – ident: ref19 doi: 10.1109/CVPR.2008.4587584 – ident: ref145 doi: 10.1109/LRA.2020.2974392 – ident: ref81 doi: 10.1109/TIP.2017.2745103 – year: 2019 ident: ref72 article-title: Online multi-object tracking framework with the GMPHD filter and occlusion group management publication-title: arXiv 1907 13347 – ident: ref2 doi: 10.2528/PIERB15010503 – ident: ref108 doi: 10.1109/TPAMI.2017.2691769 – ident: ref90 doi: 10.1109/WACV.2018.00128 – ident: ref126 doi: 10.1109/TPAMI.2016.2551245 – start-page: 351 year: 2018 ident: ref4 article-title: A survey of multi-object video tracking algorithms publication-title: Proc Adv Intell Sys Comput doi: 10.2991/ijcis.2019.125905651 – volume: 20 start-page: 1 year: 2019 ident: ref134 article-title: Online multi-object tracking using joint domain information in traffic scenarios publication-title: IEEE Trans Intell Transp Syst – ident: ref138 doi: 10.1109/ICCV.2017.322 – ident: ref46 doi: 10.1109/CVPR.2017.394 – ident: ref143 doi: 10.1109/ACCESS.2019.2903121 – ident: ref99 doi: 10.1007/978-81-322-1665-0_52 – ident: ref107 doi: 10.1109/ICIP.2016.7533003 – ident: ref20 doi: 10.1109/CVPR.2011.5995604 – ident: ref6 doi: 10.1016/j.neucom.2019.11.023 – ident: ref124 doi: 10.1016/j.patcog.2019.04.018 – ident: ref69 doi: 10.1109/CVPR.2016.234 – start-page: 4225 year: 2017 ident: ref57 article-title: Online multi-target tracking using recurrent neural networks publication-title: Proc AAAI Conf Artif Intell – ident: ref55 doi: 10.1109/CVPR.2018.00232 – ident: ref40 doi: 10.1109/TPAMI.2015.2505309 – year: 2020 ident: ref60 article-title: SDVTracker: Real-time multi-sensor association and tracking for self-driving vehicles publication-title: arXiv 2003 04447 – ident: ref58 doi: 10.1109/ICIP.2018.8451472 – ident: ref130 doi: 10.1109/LSP.2019.2940922 – year: 2019 ident: ref117 article-title: How to train your deep multi-object tracker publication-title: arXiv 1906 06618 – ident: ref74 doi: 10.1007/s11042-018-5781-3 – ident: ref82 doi: 10.1016/j.cviu.2016.05.003 – ident: ref11 doi: 10.1145/3343031.3350984 – ident: ref13 doi: 10.1109/TMM.2016.2605058 – year: 2015 ident: ref66 article-title: MOTChallenge 2015: Towards a benchmark for multi-target tracking publication-title: arXiv 1504 01942 [cs] – ident: ref39 doi: 10.1109/CVPR.2012.6247893 – ident: ref22 doi: 10.1109/CVPR.2013.241 – ident: ref110 doi: 10.1109/CVPRW.2018.00195 – ident: ref9 doi: 10.1007/s13592-011-0060-6 – ident: ref59 doi: 10.1109/ICCV.2019.00245 – ident: ref73 doi: 10.1109/CVPRW.2018.00169 – volume: 34 start-page: 2420 year: 2012 ident: ref18 article-title: Single and multiple object tracking using log-Euclidean Riemannian subspace and block-division appearance model publication-title: IEEE Trans Pattern Anal Mach Intell doi: 10.1109/TPAMI.2012.42 – ident: ref121 doi: 10.1109/WACV.2019.00023 – ident: ref105 doi: 10.1145/3343031.3350853 – ident: ref31 doi: 10.1109/CVPR.2017.403 – year: 2014 ident: ref1 article-title: Multiple object tracking: A literature review publication-title: arXiv 1409 7618 [cs] – year: 2016 ident: ref84 article-title: A multi-cut formulation for joint segmentation and tracking of multiple objects publication-title: arXiv 1607 06317 – start-page: 343 year: 2012 ident: ref42 article-title: GMCP-tracker: Global multi-object tracking using generalized minimum clique graphs publication-title: Proc Eur Conf Comput Vis – ident: ref48 doi: 10.1109/GlobalSIP.2016.7905816 – ident: ref52 doi: 10.1109/CVPR.2015.7299178 – ident: ref120 doi: 10.1109/TIP.2018.2843129 – year: 2019 ident: ref8 article-title: Online multiple pedestrian tracking using deep temporal appearance matching association publication-title: arXiv 1907 00831 – ident: ref7 doi: 10.1109/WMVC.2008.4544058 – year: 2018 ident: ref139 article-title: YOLOv3: An incremental improvement publication-title: arXiv 1804 02767 – ident: ref87 doi: 10.1007/978-3-030-01219-9_36 – ident: ref63 doi: 10.1109/TPAMI.2009.167 – ident: ref116 doi: 10.1109/CVPR.2017.495 – ident: ref147 doi: 10.1109/IVS.2019.8813779 – ident: ref78 doi: 10.1016/j.neucom.2015.07.028 – ident: ref43 doi: 10.1109/CVPR.2015.7299036 – ident: ref54 doi: 10.1109/ICCV.2015.533 – start-page: 1 year: 2014 ident: ref65 article-title: Aggregate channel features for multi-view face detection publication-title: Proc IEEE Int Joint Conf Biometrics – ident: ref135 doi: 10.1109/TCSVT.2018.2882192 – year: 2019 ident: ref70 article-title: CVPR19 tracking and detection challenge: How crowded can it get? publication-title: arXiv 1906 04567 – ident: ref111 doi: 10.1109/CVPR.2014.161 – ident: ref15 doi: 10.1109/TPAMI.2013.221 – ident: ref91 doi: 10.1007/s11263-013-0666-4 – ident: ref21 doi: 10.1109/CVPR.2015.7298718 – ident: ref141 doi: 10.1109/TPAMI.2019.2929520 – start-page: 91 year: 2015 ident: ref68 article-title: Faster R-CNN: Towards real-time object detection with region proposal networks publication-title: Proc Adv Neural Inf Process Syst – ident: ref16 doi: 10.1016/j.cviu.2016.12.003 – ident: ref97 doi: 10.1109/CVPR.2010.5540102 – ident: ref128 doi: 10.1155/2008/246309 – ident: ref98 doi: 10.1109/ROBIO.2015.7419004 – ident: ref79 doi: 10.1109/ICPCSI.2017.8392001 – ident: ref123 doi: 10.1109/ICCV.2017.41 – start-page: 740 year: 2014 ident: ref27 article-title: Microsoft COCO: Common objects in context publication-title: Proc Eur Conf Comput Vis – ident: ref80 doi: 10.1109/TPAMI.2013.103 – ident: ref38 doi: 10.1109/CVPR.2011.5995311 – ident: ref132 doi: 10.1109/CVPR.2017.206 – ident: ref109 doi: 10.1016/j.neucom.2018.02.068 – ident: ref118 doi: 10.1109/CVPR.2017.142 – ident: ref35 doi: 10.1109/CVPR.2011.5995587 – ident: ref29 doi: 10.1007/978-3-642-33715-4_43 – year: 2020 ident: ref144 article-title: Multi-object tracking via end-to-end tracklet searching and ranking publication-title: arXiv 2003 02795 – ident: ref88 doi: 10.1109/ICCV.2009.5459278 – ident: ref30 doi: 10.1109/ICIP.2017.8296962 – ident: ref37 doi: 10.1109/TCSVT.2018.2825679 |
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| Snippet | Multiple pedestrian tracking (MPT) has gained significant attention due to its huge potential in a commercial application. It aims to predict multiple... |
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| SubjectTerms | Algorithms Computer vision data association Machine learning multiple pedestrian tracking Noise measurement Radar tracking Task analysis Tracking tracking-by-detection Trajectory Video sequences Visualization |
| Title | A Survey of Multiple Pedestrian Tracking Based on Tracking-by-Detection Framework |
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