Real-time human body tracking based on data fusion from multiple RGB-D sensors
In this work we present a human pose estimation method based on the skeleton fusion and tracking using multiple RGB-D sensors. The proposed method considers the skeletons provided by each RGB-D device and constructs an improved skeleton, taking into account the quality measures provided by the senso...
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| Published in | Multimedia tools and applications Vol. 76; no. 3; pp. 4249 - 4271 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
New York
Springer US
01.02.2017
Springer Nature B.V |
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| Abstract | In this work we present a human pose estimation method based on the skeleton fusion and tracking using multiple RGB-D sensors. The proposed method considers the skeletons provided by each RGB-D device and constructs an improved skeleton, taking into account the quality measures provided by the sensors at two different levels: the whole skeleton and each joint individually. Then, each joint is tracked by a Kalman filter, resulting in a smooth tracking performance. We have also developed a new dataset consisting of six subjects performing seven different gestures, recorded with four Kinect devices simultaneously. Experimental results performed on this dataset show that the system obtains better smoothness results than the most representative methods found in the literature. The proposed system operates at a processing rate of 25 frames per second (including the whole algorithm loop, i.e., data acquisition and processing) without the explicit use of the multithreading capabilities of the system. |
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| AbstractList | In this work we present a human pose estimation method based on the skeleton fusion and tracking using multiple RGB-D sensors. The proposed method considers the skeletons provided by each RGB-D device and constructs an improved skeleton, taking into account the quality measures provided by the sensors at two different levels: the whole skeleton and each joint individually. Then, each joint is tracked by a Kalman filter, resulting in a smooth tracking performance. We have also developed a new dataset consisting of six subjects performing seven different gestures, recorded with four Kinect devices simultaneously. Experimental results performed on this dataset show that the system obtains better smoothness results than the most representative methods found in the literature. The proposed system operates at a processing rate of 25 frames per second (including the whole algorithm loop, i.e., data acquisition and processing) without the explicit use of the multithreading capabilities of the system. |
| Author | Núñez, Juan C. Cabido, Raúl Pantrigo, Juan J. Montemayor, Antonio S. |
| Author_xml | – sequence: 1 givenname: Juan C. surname: Núñez fullname: Núñez, Juan C. email: jc.nunezm@alumnos.urjc.es organization: Universidad Rey Juan Carlos – sequence: 2 givenname: Raúl surname: Cabido fullname: Cabido, Raúl organization: Universidad Rey Juan Carlos – sequence: 3 givenname: Antonio S. surname: Montemayor fullname: Montemayor, Antonio S. organization: Universidad Rey Juan Carlos – sequence: 4 givenname: Juan J. surname: Pantrigo fullname: Pantrigo, Juan J. organization: Universidad Rey Juan Carlos |
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| CitedBy_id | crossref_primary_10_1016_j_measurement_2019_107455 crossref_primary_10_1016_j_imavis_2019_05_003 crossref_primary_10_1007_s11042_019_7433_7 crossref_primary_10_1016_j_cag_2024_103917 crossref_primary_10_1016_j_neucom_2018_10_009 crossref_primary_10_1007_s10515_021_00293_y crossref_primary_10_1016_j_heliyon_2023_e21606 crossref_primary_10_3390_s22093155 crossref_primary_10_1186_s13673_020_00256_4 crossref_primary_10_3389_fspor_2021_809898 crossref_primary_10_1155_2022_3419951 crossref_primary_10_1016_j_ijleo_2020_164563 |
| Cites_doi | 10.1109/CVPR.2016.156 10.1115/1.3662552 10.1115/1.4025810 10.1007/978-3-319-08651-4_2 10.1115/1.4025404 10.1109/CVPR.2016.108 10.1109/CVPRW.2012.6239174 10.1109/CVPR.2016.109 10.1109/IROS.2012.6385968 10.1007/s11263-016-0880-y 10.1016/j.patrec.2013.02.006 10.1007/3-540-45053-X_1 10.1007/978-3-642-44964-2_8 10.1109/CVPR.2016.153 10.1109/TPAMI.2013.250 10.1109/TCSVT.2016.2540978 10.1145/2643188.2643195 10.1007/978-3-319-04114-8_40 10.3233/ICA-160523 10.1109/CVPR.2000.854758 10.1007/978-1-4471-0679-1 10.1109/CVPR.2011.5995316 |
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| References | Berger K (2013) The role of RGB-D benchmark datasets: an overview. Comput Res Reposit:4321–4326 Bertinetto L, Valmadre J, Golodetz S, Miksik O, Torr PHS (2016) Staple: complementary learners for real-time tracking. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), pp 1401–1409 Bünger M (2013) Evaluation of skeleton trackers and gesture recognition for human-robot interaction. Master Thesis, Aalborg University Hong Yoon J, Lee C-R, Yang M-H, Yoon K-J (2016) Online multi-object tracking via structural constraint event aggregation. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), pp 1392–1400 Lacabex B, Cuesta A, Montemayor AS, Pantrigo JJ (2016) Lightweight tracking-by-detection system for multiple pedestrian targets. Integrated computer-aided engineering. In press PerniciFDel BimboAObject tracking by oversampling local featuresIEEE Trans Pattern Anal Mach Intell201436122538255110.1109/TPAMI.2013.250 Willianson B, LaViola J, Roberts T, Garrity P. (2012) Multi-kinect tracking for dismounted soldier training. In: Interservice/industry training, simulation and education conference (I/ITSEC) Yeung K-Y, Kwok T-H, Wang CC (2013) Improved skeleton tracking by duplex kinects: a practical approach for real-time applications. J Comput Inf Sci Eng 13(4) Zhang B, Li Z, Perina A, Del Bue A, Murino V (in press 2016) Adaptive local movement modelling (ALMM) for object tracking. IEEE Trans Circuits Syst Video Technol. doi:10.1109/TCSVT.2016.2540978 ChenLWeiHFerrymanJA survey of human motion analysis using depth imageryPattern Recogn Lett201334151995200610.1016/j.patrec.2013.02.006 KalmanREA new approach to linear filtering and prediction problemsJ Basic Eng1960823510.1115/1.3662552 Deutscher J, Blake A, Reid I (2000) Articulated body motion capture by annealed particle filtering. In: Proceedings of the IEEE conference on computer vision and pattern recognition, vol 2, pp 126–133 MoratoCKaipaKNZhaoBGuptaSKToward safe human robot collaboration by using multiple kinects based real-time human trackingJ Comput Inform Sci Eng201414101100610.1115/1.4025810 Wang L, Ouyang W, Wang X, Lu H (2016) STCT: sequentially training convolutional networks for visual tracking. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), pp 1373–1381 Ye M, Zhang Q, Wang L, Zhu J, Yang R, Gall J (2013) A survey on human motion analysis from depth data. In: Grzegorzek M, Theobalt C, Reinhard K, Andreas K (eds) Time-of-flight and depth imaging. Sensors, algorithms, and applications, lecture notes in computer science, pp 149–187 MacCormick J (2002) Stochastic algorithm for visual tracking. Springer Papadopoulos G, Axenopoulos A, Daras P (2014) Real-time skeleton-tracking-based human action recognition using kinect data. In: Gurrin C, Hopfgartner F, Hurst W, Johansen H, Lee H, Connor N (eds) MultiMedia modeling, lecture notes in computer science, pp 473–483 Behún K, Herout A, Páldy A (2014) Kinect-supported dataset creation for human pose estimation. In: Proceedings of the 30th Spring conference on computer graphics (SCCG), pp 55–62 MacCormick J, Isard M (2000) Partitioned sampling, articulated objects and interface-quality hand tracking. In: Proceedings of the 6th European conference on computer vision (ECCV), part II, pp 3–19 Destelle F, Ahmadi A, O’Connor N, Moran K, Chatzitofis A, Zarpalas D, Daras P (2014) Low-cost accurate skeleton tracking based on fusion of kinect and wearable inertial sensors. In: Proceedings of signal processing conference (EUSIPCO), pp 371–375 Zhang L, Sturm J, Cremers D, Lee D. (2012) Real-time human motion tracking using multiple depth cameras. In: Proceedings of the international conference on intelligent robot systems (IROS) Souvenir R, Hajja A, Spurlock S (2012) Gamesourcing to acquire labeled human pose estimation data. In: IEEE computer society conference on computer vision and pattern recognition workshops (CVPRW), pp 1--6 Yu H, Zhou Y, Simmons J, Przybyla C P, Lin Y, Fan X, Mi Y, Wang S (2016) Groupwise tracking of crowded similar-appearance targets from low-continuity image sequences. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), pp 952–960 Shotton J, Fitzgibbon A, Cook M, Sharp T, Finocchio M, Moore R, Kipman A, Blake A (2011) Real-time human pose recognition in parts from single depth images, In: Proceedings of the 2011 IEEE conference on computer vision and pattern recognition, pp 1297–1304 Zhang B, Perina A, Li Z, Murino V, Liu J, Ji R (in press 2016) Bounding multiple Gaussians uncertainty with application to object tracking. Int J Comput Vis. doi:10.1007/s11263-016-0880-y Berger K (2014) A state of the art report on multiple RGB-D sensor research and on publicly available RGB-D datasets. In: Computer vision and machine learning with RGB-d sensors. Springer International Publishing, pp 27–44 Zhu G, Porikli F, Hongdong L (2016) Beyond local search: tracking objects everywhere with instance-specific proposals 3759_CR27 3759_CR26 3759_CR25 3759_CR24 C Morato (3759_CR14) 2014; 14 3759_CR23 3759_CR22 3759_CR21 3759_CR20 L Chen (3759_CR6) 2013; 34 RE Kalman (3759_CR10) 1960; 82 3759_CR8 3759_CR9 3759_CR15 3759_CR7 3759_CR13 3759_CR19 3759_CR18 3759_CR17 3759_CR12 3759_CR11 3759_CR1 F Pernici (3759_CR16) 2014; 36 3759_CR4 3759_CR5 3759_CR2 3759_CR3 |
| References_xml | – reference: KalmanREA new approach to linear filtering and prediction problemsJ Basic Eng1960823510.1115/1.3662552 – reference: MacCormick J (2002) Stochastic algorithm for visual tracking. Springer – reference: Bünger M (2013) Evaluation of skeleton trackers and gesture recognition for human-robot interaction. Master Thesis, Aalborg University – reference: ChenLWeiHFerrymanJA survey of human motion analysis using depth imageryPattern Recogn Lett201334151995200610.1016/j.patrec.2013.02.006 – reference: PerniciFDel BimboAObject tracking by oversampling local featuresIEEE Trans Pattern Anal Mach Intell201436122538255110.1109/TPAMI.2013.250 – reference: Deutscher J, Blake A, Reid I (2000) Articulated body motion capture by annealed particle filtering. In: Proceedings of the IEEE conference on computer vision and pattern recognition, vol 2, pp 126–133 – reference: MoratoCKaipaKNZhaoBGuptaSKToward safe human robot collaboration by using multiple kinects based real-time human trackingJ Comput Inform Sci Eng201414101100610.1115/1.4025810 – reference: Berger K (2014) A state of the art report on multiple RGB-D sensor research and on publicly available RGB-D datasets. In: Computer vision and machine learning with RGB-d sensors. Springer International Publishing, pp 27–44 – reference: Zhang B, Perina A, Li Z, Murino V, Liu J, Ji R (in press 2016) Bounding multiple Gaussians uncertainty with application to object tracking. Int J Comput Vis. doi:10.1007/s11263-016-0880-y – reference: Willianson B, LaViola J, Roberts T, Garrity P. (2012) Multi-kinect tracking for dismounted soldier training. In: Interservice/industry training, simulation and education conference (I/ITSEC) – reference: Zhu G, Porikli F, Hongdong L (2016) Beyond local search: tracking objects everywhere with instance-specific proposals – reference: Zhang L, Sturm J, Cremers D, Lee D. (2012) Real-time human motion tracking using multiple depth cameras. In: Proceedings of the international conference on intelligent robot systems (IROS) – reference: Zhang B, Li Z, Perina A, Del Bue A, Murino V (in press 2016) Adaptive local movement modelling (ALMM) for object tracking. IEEE Trans Circuits Syst Video Technol. doi:10.1109/TCSVT.2016.2540978 – reference: Shotton J, Fitzgibbon A, Cook M, Sharp T, Finocchio M, Moore R, Kipman A, Blake A (2011) Real-time human pose recognition in parts from single depth images, In: Proceedings of the 2011 IEEE conference on computer vision and pattern recognition, pp 1297–1304 – reference: Bertinetto L, Valmadre J, Golodetz S, Miksik O, Torr PHS (2016) Staple: complementary learners for real-time tracking. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), pp 1401–1409 – reference: Yu H, Zhou Y, Simmons J, Przybyla C P, Lin Y, Fan X, Mi Y, Wang S (2016) Groupwise tracking of crowded similar-appearance targets from low-continuity image sequences. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), pp 952–960 – reference: Behún K, Herout A, Páldy A (2014) Kinect-supported dataset creation for human pose estimation. In: Proceedings of the 30th Spring conference on computer graphics (SCCG), pp 55–62 – reference: Ye M, Zhang Q, Wang L, Zhu J, Yang R, Gall J (2013) A survey on human motion analysis from depth data. In: Grzegorzek M, Theobalt C, Reinhard K, Andreas K (eds) Time-of-flight and depth imaging. Sensors, algorithms, and applications, lecture notes in computer science, pp 149–187 – reference: MacCormick J, Isard M (2000) Partitioned sampling, articulated objects and interface-quality hand tracking. In: Proceedings of the 6th European conference on computer vision (ECCV), part II, pp 3–19 – reference: Yeung K-Y, Kwok T-H, Wang CC (2013) Improved skeleton tracking by duplex kinects: a practical approach for real-time applications. J Comput Inf Sci Eng 13(4) – reference: Berger K (2013) The role of RGB-D benchmark datasets: an overview. Comput Res Reposit:4321–4326 – reference: Lacabex B, Cuesta A, Montemayor AS, Pantrigo JJ (2016) Lightweight tracking-by-detection system for multiple pedestrian targets. Integrated computer-aided engineering. In press – reference: Papadopoulos G, Axenopoulos A, Daras P (2014) Real-time skeleton-tracking-based human action recognition using kinect data. In: Gurrin C, Hopfgartner F, Hurst W, Johansen H, Lee H, Connor N (eds) MultiMedia modeling, lecture notes in computer science, pp 473–483 – reference: Destelle F, Ahmadi A, O’Connor N, Moran K, Chatzitofis A, Zarpalas D, Daras P (2014) Low-cost accurate skeleton tracking based on fusion of kinect and wearable inertial sensors. In: Proceedings of signal processing conference (EUSIPCO), pp 371–375 – reference: Hong Yoon J, Lee C-R, Yang M-H, Yoon K-J (2016) Online multi-object tracking via structural constraint event aggregation. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), pp 1392–1400 – reference: Souvenir R, Hajja A, Spurlock S (2012) Gamesourcing to acquire labeled human pose estimation data. In: IEEE computer society conference on computer vision and pattern recognition workshops (CVPRW), pp 1--6 – reference: Wang L, Ouyang W, Wang X, Lu H (2016) STCT: sequentially training convolutional networks for visual tracking. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), pp 1373–1381 – ident: 3759_CR4 doi: 10.1109/CVPR.2016.156 – volume: 82 start-page: 35 year: 1960 ident: 3759_CR10 publication-title: J Basic Eng doi: 10.1115/1.3662552 – volume: 14 start-page: 011006 issue: 1 year: 2014 ident: 3759_CR14 publication-title: J Comput Inform Sci Eng doi: 10.1115/1.4025810 – ident: 3759_CR9 – ident: 3759_CR3 doi: 10.1007/978-3-319-08651-4_2 – ident: 3759_CR22 doi: 10.1115/1.4025404 – ident: 3759_CR27 doi: 10.1109/CVPR.2016.108 – ident: 3759_CR7 – ident: 3759_CR17 doi: 10.1109/CVPRW.2012.6239174 – ident: 3759_CR5 – ident: 3759_CR2 – ident: 3759_CR23 doi: 10.1109/CVPR.2016.109 – ident: 3759_CR24 doi: 10.1109/IROS.2012.6385968 – ident: 3759_CR25 doi: 10.1007/s11263-016-0880-y – volume: 34 start-page: 1995 issue: 15 year: 2013 ident: 3759_CR6 publication-title: Pattern Recogn Lett doi: 10.1016/j.patrec.2013.02.006 – ident: 3759_CR12 doi: 10.1007/3-540-45053-X_1 – ident: 3759_CR20 – ident: 3759_CR21 doi: 10.1007/978-3-642-44964-2_8 – ident: 3759_CR19 doi: 10.1109/CVPR.2016.153 – volume: 36 start-page: 2538 issue: 12 year: 2014 ident: 3759_CR16 publication-title: IEEE Trans Pattern Anal Mach Intell doi: 10.1109/TPAMI.2013.250 – ident: 3759_CR26 doi: 10.1109/TCSVT.2016.2540978 – ident: 3759_CR1 doi: 10.1145/2643188.2643195 – ident: 3759_CR15 doi: 10.1007/978-3-319-04114-8_40 – ident: 3759_CR11 doi: 10.3233/ICA-160523 – ident: 3759_CR8 doi: 10.1109/CVPR.2000.854758 – ident: 3759_CR13 doi: 10.1007/978-1-4471-0679-1 – ident: 3759_CR18 doi: 10.1109/CVPR.2011.5995316 |
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| SubjectTerms | Algorithms Cameras Computer Communication Networks Computer Science Computer vision Data integration Data Structures and Information Theory Devices Frames per second Human body Methods Movement Multimedia Multimedia communications Multimedia Information Systems Position tracking Sensors Smoothness Special Purpose and Application-Based Systems Studies Tracking |
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| Title | Real-time human body tracking based on data fusion from multiple RGB-D sensors |
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