MOTChallenge: A Benchmark for Single-Camera Multiple Target Tracking

Standardized benchmarks have been crucial in pushing the performance of computer vision algorithms, especially since the advent of deep learning. Although leaderboards should not be over-claimed, they often provide the most objective measure of performance and are therefore important guides for rese...

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Vydáno v:International journal of computer vision Ročník 129; číslo 4; s. 845 - 881
Hlavní autoři: Dendorfer, Patrick, Os̆ep, Aljos̆a, Milan, Anton, Schindler, Konrad, Cremers, Daniel, Reid, Ian, Roth, Stefan, Leal-Taixé, Laura
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York Springer US 01.04.2021
Springer
Springer Nature B.V
Témata:
Algorithms
Artificial Intelligence
Autonomous cars
Benchmarks
Cameras
Computer Imaging
Computer Science
Computer vision
Error analysis
Image Processing and Computer Vision
Labels
Machine learning
Machine vision
Multiple target tracking
Pattern Recognition
Pattern Recognition and Graphics
Pedestrians
Performance evaluation
Special Issue on Performance Evaluation in Computer Vision
Visibility
Vision
Evaluation
Computer vision
MOTA
MOTChallenge
Multi-object-tracking
ISSN:0920-5691, 1573-1405
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Abstract Standardized benchmarks have been crucial in pushing the performance of computer vision algorithms, especially since the advent of deep learning. Although leaderboards should not be over-claimed, they often provide the most objective measure of performance and are therefore important guides for research. We present MOTChallenge , a benchmark for single-camera Multiple Object Tracking (MOT) launched in late 2014, to collect existing and new data and create a framework for the standardized evaluation of multiple object tracking methods. The benchmark is focused on multiple people tracking, since pedestrians are by far the most studied object in the tracking community, with applications ranging from robot navigation to self-driving cars. This paper collects the first three releases of the benchmark: (i) MOT15 , along with numerous state-of-the-art results that were submitted in the last years, (ii) MOT16 , which contains new challenging videos, and (iii) MOT17 , that extends MOT16 sequences with more precise labels and evaluates tracking performance on three different object detectors. The second and third release not only offers a significant increase in the number of labeled boxes, but also provide labels for multiple object classes beside pedestrians, as well as the level of visibility for every single object of interest. We finally provide a categorization of state-of-the-art trackers and a broad error analysis. This will help newcomers understand the related work and research trends in the MOT community, and hopefully shed some light into potential future research directions.
AbstractList Standardized benchmarks have been crucial in pushing the performance of computer vision algorithms, especially since the advent of deep learning. Although leaderboards should not be over-claimed, they often provide the most objective measure of performance and are therefore important guides for research. We present MOTChallenge , a benchmark for single-camera Multiple Object Tracking (MOT) launched in late 2014, to collect existing and new data and create a framework for the standardized evaluation of multiple object tracking methods. The benchmark is focused on multiple people tracking, since pedestrians are by far the most studied object in the tracking community, with applications ranging from robot navigation to self-driving cars. This paper collects the first three releases of the benchmark: (i) MOT15 , along with numerous state-of-the-art results that were submitted in the last years, (ii) MOT16 , which contains new challenging videos, and (iii) MOT17 , that extends MOT16 sequences with more precise labels and evaluates tracking performance on three different object detectors. The second and third release not only offers a significant increase in the number of labeled boxes, but also provide labels for multiple object classes beside pedestrians, as well as the level of visibility for every single object of interest. We finally provide a categorization of state-of-the-art trackers and a broad error analysis. This will help newcomers understand the related work and research trends in the MOT community, and hopefully shed some light into potential future research directions.
Standardized benchmarks have been crucial in pushing the performance of computer vision algorithms, especially since the advent of deep learning. Although leaderboards should not be over-claimed, they often provide the most objective measure of performance and are therefore important guides for research. We present MOTChallenge, a benchmark for single-camera Multiple Object Tracking (MOT) launched in late 2014, to collect existing and new data and create a framework for the standardized evaluation of multiple object tracking methods. The benchmark is focused on multiple people tracking, since pedestrians are by far the most studied object in the tracking community, with applications ranging from robot navigation to self-driving cars. This paper collects the first three releases of the benchmark: (i) MOT15, along with numerous state-of-the-art results that were submitted in the last years, (ii) MOT16, which contains new challenging videos, and (iii) MOT17, that extends MOT16 sequences with more precise labels and evaluates tracking performance on three different object detectors. The second and third release not only offers a significant increase in the number of labeled boxes, but also provide labels for multiple object classes beside pedestrians, as well as the level of visibility for every single object of interest. We finally provide a categorization of state-of-the-art trackers and a broad error analysis. This will help newcomers understand the related work and research trends in the MOT community, and hopefully shed some light into potential future research directions.
Audience Academic
Author Roth, Stefan
Cremers, Daniel
Reid, Ian
Milan, Anton
Os̆ep, Aljos̆a
Schindler, Konrad
Dendorfer, Patrick
Leal-Taixé, Laura
Author_xml – sequence: 1
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  orcidid: 0000-0002-4623-8749
  surname: Dendorfer
  fullname: Dendorfer, Patrick
  email: patrick.dendorfer@tum.de
  organization: Technical University Munich
– sequence: 2
  givenname: Aljos̆a
  surname: Os̆ep
  fullname: Os̆ep, Aljos̆a
  organization: Technical University Munich
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  givenname: Anton
  surname: Milan
  fullname: Milan, Anton
  organization: Amazon Research
– sequence: 4
  givenname: Konrad
  surname: Schindler
  fullname: Schindler, Konrad
  organization: ETH Zürich
– sequence: 5
  givenname: Daniel
  surname: Cremers
  fullname: Cremers, Daniel
  organization: Technical University Munich
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  surname: Reid
  fullname: Reid, Ian
  organization: The University of Adelaide
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  givenname: Stefan
  surname: Roth
  fullname: Roth, Stefan
  organization: Technical University of Darmstadt
– sequence: 8
  givenname: Laura
  surname: Leal-Taixé
  fullname: Leal-Taixé, Laura
  organization: Technical University Munich
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Cites_doi 10.1109/CVPR.2005.177
10.1109/WACV.2016.7477566
10.1109/ICCV.2017.41
10.1109/AVSS.2017.8078516
10.1109/TPAMI.2018.2876253
10.1109/CVPRW.2013.111
10.1109/TIP.2017.2745103
10.1109/AVSS.2017.8078552
10.1109/ICIP.2015.7351235
10.1364/JOSAA.34.000280
10.1109/CVPR.2006.100
10.1109/AVSS.2012.59
10.1109/UV.2018.8642156
10.1007/978-3-319-48881-3_4
10.1109/CVPR.2015.7299178
10.1109/CVPR.2017.394
10.1109/CVPRW.2018.00192
10.1109/CVPR.2012.6248074
10.1109/CVPRW.2017.266
10.1109/CVPR.2018.00935
10.1109/CVPR.2011.5995604
10.1109/ICCV.2015.534
10.3390/s19030559
10.1109/TPAMI.2015.2505309
10.1007/s11263-016-0960-z
10.1109/AVSS.2016.7738059
10.1049/iet-cvi.2016.0068
10.1109/ICRA.2016.7487371
10.1007/978-3-030-58558-7_26
10.1109/ACCESS.2019.2921975
10.1007/s11263-006-7899-4
10.1109/CVPR.2017.145
10.1007/978-3-319-46448-0_45
10.1007/s11263-014-0733-5
10.1109/ACCESS.2020.2975912
10.1007/s11263-010-0390-2
10.1007/978-3-030-01237-3_13
10.1109/CVPR.2016.234
10.1109/TCSVT.2018.2881123
10.1109/ICCV.2017.518
10.1109/ACCESS.2018.2889442
10.1007/978-3-319-48881-3_2
10.1109/CVPR42600.2020.00628
10.1109/ICCV.2019.00409
10.5220/0006564504290438
10.1109/ICCV.2011.6126532
10.1109/ICIP.2019.8803140
10.1016/j.cviu.2016.05.003
10.1023/A:1014573219977
10.1109/CVPR.2014.453
10.1109/CVPR.2008.4587584
10.1007/978-3-319-46484-8_47
10.1109/CVPR.2011.5995347
10.1109/ICCV.2019.00627
10.1109/CVPR.2017.206
10.1109/TCSVT.2020.2975842
10.1016/j.neucom.2019.08.008
10.1109/CVPR.2019.00895
10.1109/CVPR.2008.4587581
10.1109/CVPR.2014.283
10.14569/IJACSA.2017.081129
10.1007/978-3-319-10593-2_47
10.1109/WACV.2015.12
10.1109/AVSS.2018.8639078
10.1609/aaai.v31i1.11194
10.1109/CVPR.2014.167
10.1109/TPAMI.2014.2300479
10.1109/CVPR.2019.00477
10.1109/ACCESS.2018.2879535
10.1109/TITS.2019.2892413
10.1109/ICRA.2016.7487180
10.1109/TCSVT.2018.2825679
10.1109/ICCV.2015.169
10.1007/978-3-030-01228-1_23
10.1109/WACV.2015.17
10.1109/CVPRW.2016.55
10.1109/AVSS.2017.8078481
10.1109/CVPR.2015.7299138
10.1109/CVPR.2014.159
10.1109/ICCV.2019.00103
10.1109/CVPR.2018.00542
10.1109/ICCV.2011.6126516
10.1109/ACCESS.2019.2932301
10.1109/TCSVT.2018.2882192
10.1109/CVPR.2005.453
10.1145/3343031.3350853
10.1109/TIP.2018.2843129
10.1109/ACCESS.2019.2953276
10.1109/CVPRW.2009.5206631
10.1109/SITIS.2018.00021
10.1016/j.patcog.2019.04.018
10.1109/ICCV.2013.286
10.1109/ACCESS.2018.2881019
10.1007/978-3-319-48881-3_5
10.1109/ICCV.2015.349
10.1109/PETS-WINTER.2009.5399556
10.1109/CVPR.2016.155
10.1109/CVPR.2017.403
10.1109/TAC.1979.1102177
10.1109/TIP.2020.2993073
10.1016/j.jvcir.2019.01.026
10.1109/AVSS.2010.90
10.1109/TPAMI.2017.2691769
10.1109/ACCESS.2018.2816805
10.1109/CVPR.2015.7299036
10.1109/TPAMI.2013.103
10.1109/ICCVW.2011.6130233
10.1109/CVPRW.2016.59
10.1002/nav.3800020109
10.1109/TMM.2019.2902480
10.1007/978-3-319-48881-3_7
10.1109/ICCV.2015.533
10.1109/WACV.2018.00057
10.1109/CVPR42600.2020.00252
10.1007/s11263-015-0816-y
10.1155/2008/246309
10.1007/s11263-018-01147-z
10.1109/CVPR.2010.5540156
10.1109/ICME.2018.8486454
10.1109/LSP.2019.2940922
10.1016/j.cviu.2020.102907
10.1109/ICIP.2016.7533003
10.1109/TPAMI.2013.185
10.1007/978-3-030-20890-5_39
10.1109/ICCE-Asia.2016.7804800
10.1109/AVSS.2017.8078517
10.1109/CVPR.2006.19
10.1109/CVPR42600.2020.00682
10.1109/WACV.2019.00023
10.1109/ICCE-ASIA.2018.8552105
10.1109/CVPR42600.2020.00250
10.1109/ICCV.2015.347
10.1109/TSP.2008.920469
10.1007/978-3-319-48881-3_8
10.1109/CVPRW.2019.00105
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References Chu, P., Fan, H., Tan, C. C., & Ling, H. (2019). Online multi-object tracking with instance-aware tracker and dynamic model refreshment. In Winter conference on applications of computer vision.
Baisa, N. L. (2018). Online multi-target visual tracking using a HISP filter. In International joint conference on computer vision, imaging and computer graphics theory and applications.
Ess, A., Leibe, B., Schindler, K., & Van Gool, L. (2008). A mobile vision system for robust multi-person tracking. In Conference on computer vision and pattern recognition.
Bae, S.-H., & Yoon, K.-J. (2014). Robust online multi-object tracking based on tracklet confidence and online discriminative appearance learning. In Conference on computer vision and pattern recognition.
Dehghan, A., Assari, S. M., & Shah, M. (2015) GMMCP-tracker: Globally optimal generalized maximum multi clique problem for multiple object tracking. In Conference on computer vision and pattern recognition workshops.
Milan, A., Rezatofighi, S. H., Dick, A., Reid, I., & Schindler, K. (2017). Online multi-target tracking using recurrent neural networks. In Conference on artificial on intelligence.
BaisaNLWallaceADevelopment of a n-type GM-PHD filter for multiple target, multiple type visual trackingJournal of Visual Communication and Image Representation20195925727110.1016/j.jvcir.2019.01.026
MahgoubHMostafaKWassifKTFaragIMulti-target tracking using hierarchical convolutional features and motion cuesInternational Journal of Advanced Computer Science & Applications201781121722210.14569/IJACSA.2017.081129
Leal-Taixé, L., Pons-Moll, G., & Rosenhahn, B. (2011). Everybody needs somebody: Modeling social and grouping behavior on a linear programming multiple people tracker. In International conference on computer vision workshops.
Ferryman, J., & Shahrokni, A. (2009). PETS2009: Dataset and challenge. In International workshop on performance evaluation of tracking and surveillance.
Zhang, L., Li, Y., & Nevatia, R. (2008). Global data association for multi-object tracking using network flows. In Conference on computer vision and pattern recognition.
Chen, W., Chen, X., Zhang, J., & Huang, K. (2017b). Beyond triplet loss: A deep quadruplet network for person re-identification. In Conference on computer vision and pattern recognition.
Sun, P., Kretzschmar, H., Dotiwalla, X., Chouard, A., Patnaik, V., Tsui, P., Guo, J., Zhou, Y., Chai, Y., & Caine, B., et al. (2020). Scalability in perception for autonomous driving: Waymo open dataset. In Conference on computer vision and pattern recognition.
Wang, B., Wang, L., Shuai, B., Zuo, Z., Liu, T., et al. (2016). Joint learning of convolutional neural networks and temporally constrained metrics for tracklet association. In Conference on computer vision and pattern recognition.
Felzenszwalb, P. F., & Huttenlocher, D. P. (2006) Efficient belief propagation for early vision. In Conference on computer vision and pattern recognition.
Yoon, J. H., Lee, C. R., Yang, M. H., & Yoon, K. J. (2016). Online multi-object tracking via structural constraint event aggregation. In International conference on computer vision and pattern recognition.
YangMWuYJiaYA hybrid data association framework for robust online multi-object trackingTransactions on Image Processing201710.1109/TIP.2017.27451031409.94704
Taskar, B., Guestrin, C., & Koller, D. (2003). Max-margin Markov networks. In Advances in neural information processing systems.
Fagot-Bouquet, L., Audigier, R., Dhome, Y., & Lerasle, F. (2015). Online multi-person tracking based on global sparse collaborative representations. In International conference on image processing.
Ristani, E., Solera, F., Zou, R., Cucchiara, R., & Tomasi, C. (2016). Performance measures and a data set for multi-target, multi-camera tracking. In European conference on computer vision.
Girshick, R. (2015). Fast R-CNN. In International conference on computer vision.
RussakovskyODengJSuHKrauseJSatheeshSMaSImageNet large scale visual recognition challengeInternational Journal of Computer Vision20151153211252342248210.1007/s11263-015-0816-y
ShengHZhangYChenJXiongZZhangJHeterogeneous association graph fusion for target association in multiple object trackingTransactions on Circuits and Systems for Video Technology201829113269328010.1109/TCSVT.2018.2882192
Stiefelhagen, R., Bernardin, K., Bowers, R., Garofolo, J. S., Mostefa, D., & Soundararajan, P. (2006). The clear 2006 evaluation. In Multimodal technologies for perception of humans.
Chu, Q., Ouyang, W., Li, H., Wang, X., Liu, B., & Yu, N. (2017). Online multi-object tracking using CNN-based single object tracker with spatial-temporal attention mechanism. In International conference on computer vision.
Wang, G., Wang, Y., Zhang, H., Gu, R., & Hwang, J.-N. (2019). Exploit the connectivity: Multi-object tracking with trackletnet. In International conference on multimedia.
Kesten, R., Usman, M., Houston, J., Pandya, T., Nadhamuni, K., et al. (2019) Lyft level 5 av dataset 2019. https://level5.lyft.com/dataset/.
Benfold, B., & Reid, I. (2011). Unsupervised learning of a scene-specific coarse gaze estimator. In International conference on computer vision.
Tao, Y., Chen, J., Fang, Y., Masaki, I., & Horn, B. K. (2018). Adaptive spatio-temporal model based multiple object tracking in video sequences considering a moving camera. In International conference on universal village.
Ma, C., Yang, C., Yang, F., Zhuang, Y., Zhang, Z., Jia, H., & Xie, X. (2018a). Trajectory factory: Tracklet cleaving and re-connection by deep Siamese bi-GRU for multiple object tracking. In International conference on multimedia and expo.
WuHHuYWangKLiHNieLChengHInstance-aware representation learning and association for online multi-person trackingPattern Recognition201994253410.1016/j.patcog.2019.04.018
Yoon, Y.-C., Song, Y.-M., Yoon, K., & Jeon, M. (2018). Online multi-object tracking using selective deep appearance matching. In International conference on consumer electronics Asia.
Kieritz, H., Becker, S., Häbner, W., & Arens, M. (2016). Online multi-person tracking using integral channel features. In International conference on advanced video and signal based surveillance.
MilanASchindlerKRothSMulti-target tracking by discrete-continuous energy minimizationTransactions on Pattern Analysis and Machine Intelligence201638102054206810.1109/TPAMI.2015.2505309
BabaeeMLiZRigollGA dual CNN-RNN for multiple people trackingNeurocomputing2019368698310.1016/j.neucom.2019.08.008
Pedersen, M., Haurum, J. B., Bengtson, S. H., & Moeslund, T. B. (June 2020). 3D-ZEF: A 3D zebrafish tracking benchmark dataset. In Conference on computer vision and pattern recognition.
Dendorfer, P., Rezatofighi, H., Milan, A., Shi, J., Cremers, D., Reid, I., Roth, S., Schindler, K., & Leal-Taixé, L. (2020). MOT20: A benchmark for multi object tracking in crowded scenes. arXiv preprint arXiv:2003.09003.
Baisa, N. L. (2019c). Robust online multi-target visual tracking using a HISP filter with discriminative deep appearance learning. arXiv preprint arXiv:1908.03945.
Maksai, A., & Fua, P. (2019). Eliminating exposure bias and metric mismatch in multiple object tracking. In Conference on computer vision and pattern recognition.
Ren, S., He, K., Girshick, R., & Sun, J. (2015). Faster R-CNN: Towards real-time object detection with region proposal networks. In Advances in neural information processing systems.
TianWLauerMChenLOnline multi-object tracking using joint domain information in traffic scenariosTransactions on Intelligent Transportation Systems201921137438410.1109/TITS.2019.2892413
Chu, P., & Ling, H. (2019). FAMNet: Joint learning of feature, affinity and multi-dimensional assignment for online multiple object tracking. In International conference on computer vision.
Son, J., Baek, M., Cho, M., & Han, B. (2017). Multi-object tracking with quadruplet convolutional neural networks. In Conference on computer vision and pattern recognition.
Le, N., Heili, A., & Odobez, J.-M. (2016). Long-term time-sensitive costs for CRF-based tracking by detection. In European conference on computer vision workshops.
SongYYoonKYoonYYowKJeonMOnline multi-object tracking with GMPHD filter and occlusion group managementAccess2019716510316512110.1109/ACCESS.2019.2953276
Seitz, S. M., Curless, B., Diebel, J., Scharstein, D., & Szeliski, R. (2006). A comparison and evaluation of multi-view stereo reconstruction algorithms. In Conference on computer vision and pattern recognition.
Xu, J., Cao, Y., Zhang, Z., & Hu, H. (2019). Spatial-temporal relation networks for multi-object tracking. In International conference on computer vision.
Zamir, A. R., Dehghan, A., & Shah, M. (2012). GMCP-Tracker: Global multi-object tracking using generalized minimum clique graphs. In European conference on computer vision.
Geiger, A., Lenz, P., & Urtasun, R. (2012) Are we ready for autonomous driving? The KITTI vision benchmark suite. In Conference on computer vision and pattern recognition.
Mathias, M., Benenson, R., Pedersoli, M., & Gool, L. V. (2014). Face detection without bells and whistles. In European conference on computer vision workshops.
Bewley, A., Ott, L., Ramos, F., & Upcroft, B. (2016b). Alextrac: Affinity learning by exploring temporal reinforcement within association chains. In International conference on robotics and automation.
Yoon, Y., Kim, D. Y., Yoon, K., Song, Y., & Jeon, M. (2019b). Online multiple pedestrian tracking using deep temporal appearance matching association. arXiv preprint arXiv:1907.00831.
JuJKimDKuBHanDKoHOnline multi-object tracking with efficient track drift and fragmentation handlingJournal of the Optical Society of America A201734228029310.1364/JOSAA.34.000280
Boragule, A., & Jeon, M. (2017). Joint cost minimization for multi-object tracking. International conference on advanced video and signal based surveillance.
Chang, M.-F., Lambert, J., Sangkloy, P., Singh, J., Bak, S., Hartnett, A., Wang, D., Carr, P., Lucey, S., Ramanan, D., & Hays, J. (2019). Argoverse: 3D tracking and forecasting with rich maps.
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References_xml – reference: Chang, M.-F., Lambert, J., Sangkloy, P., Singh, J., Bak, S., Hartnett, A., Wang, D., Carr, P., Lucey, S., Ramanan, D., & Hays, J. (2019). Argoverse: 3D tracking and forecasting with rich maps. In Conference on computer vision and pattern recognition.
– reference: Bewley, A., Ott, L., Ramos, F., & Upcroft, B. (2016b). Alextrac: Affinity learning by exploring temporal reinforcement within association chains. In International conference on robotics and automation.
– reference: Zhou, X., Jiang, P., Wei, Z., Dong, H., & Wang, F. (2018b). Online multi-object tracking with structural invariance constraint. In British machine vision conference.
– reference: Le, N., Heili, A., & Odobez, J.-M. (2016). Long-term time-sensitive costs for CRF-based tracking by detection. In European conference on computer vision workshops.
– reference: MahgoubHMostafaKWassifKTFaragIMulti-target tracking using hierarchical convolutional features and motion cuesInternational Journal of Advanced Computer Science & Applications201781121722210.14569/IJACSA.2017.081129
– reference: Stiefelhagen, R., Bernardin, K., Bowers, R., Garofolo, J. S., Mostefa, D., & Soundararajan, P. (2006). The clear 2006 evaluation. In Multimodal technologies for perception of humans.
– reference: Huang, G. B., Ramesh, M., Berg, T., & Learned-Miller, E. (2007). Labeled faces in the wild: A database for studying face recognition in unconstrained environments. Technical Report 07-49, University of Massachussetts, Amherst.
– reference: DollárPAppelRBelongieSPeronaPFast feature pyramids for object detectionTransactions on Pattern Analysis and Machine Intelligence20143681532154510.1109/TPAMI.2014.2300479
– reference: BaeS-HYoonK-JConfidence-based data association and discriminative deep appearance learning for robust online multi-object trackingTransactions on Pattern Analysis and Machine Intelligence201840359561010.1109/TPAMI.2017.2691769
– reference: Eiselein, V., Arp, D., Pätzold, M., & Sikora, T. (2012). Real-time multi-human tracking using a probability hypothesis density filter and multiple detectors. In International conference on advanced video and signal-based surveillance.
– reference: Ess, A., Leibe, B., Schindler, K., & Van Gool, L. (2008). A mobile vision system for robust multi-person tracking. In Conference on computer vision and pattern recognition.
– reference: MilanARothSSchindlerKContinuous energy minimization for multitarget trackingTransactions on Pattern Analysis and Machine Intelligence2014361587210.1109/TPAMI.2013.103
– reference: Leal-Taixé, L., Pons-Moll, G., & Rosenhahn, B. (2011). Everybody needs somebody: Modeling social and grouping behavior on a linear programming multiple people tracker. In International conference on computer vision workshops.
– reference: Yoon, Y., Boragule, A., Song, Y., Yoon, K., & Jeon, M. (2018a). Online multi-object tracking with historical appearance matching and scene adaptive detection filtering. In International conference on advanced video and signal based surveillance.
– reference: Wang, B., Wang, L., Shuai, B., Zuo, Z., Liu, T., et al. (2016). Joint learning of convolutional neural networks and temporally constrained metrics for tracklet association. In Conference on computer vision and pattern recognition.
– reference: Ma, L., Tang, S., Black, M. J., & Van Gool, L. (2018b). Customized multi-person tracker. In Asian conference on computer vision.
– reference: Battaglia, P., Pascanu, R., Lai, M., Rezende, D. J., et al. (2016). Interaction networks for learning about objects, relations and physics. In Advances in neural information processing systems.
– reference: ScharsteinDSzeliskiRA taxonomy and evaluation of dense two-frame stereo correspondence algorithmsInternational Journal of Computer Vision200247174210.1023/A:1014573219977
– reference: Ferryman, J., & Ellis, A. (2010) PETS2010: Dataset and challenge. In International conference on advanced video and signal based surveillance.
– reference: McLaughlin, N., Martinez Del Rincon, J., Miller, P. (2015). Enhancing linear programming with motion modeling for multi-target tracking. In Winter conference on applications of computer vision.
– reference: Ban, Y., Ba, S., Alameda-Pineda, X., & Horaud, R. (2016). Tracking multiple persons based on a variational Bayesian model. In European conference on computer vision workshops.
– reference: Sadeghian, A., Alahi, A., Savarese, S. (2017). Tracking the untrackable: Learning to track multiple cues with long-term dependencies. In International conference on computer vision.
– reference: ShengHZhangXZhangYWuYChenJEnhanced association with supervoxels in multiple hypothesis trackingAccess201872107211710.1109/ACCESS.2018.2881019
– reference: Sanchez-Matilla, R., Cavallaro, A. (2019). A predictor of moving objects for first-person vision. In International conference on image processing.
– reference: Milan, A., Rezatofighi, S. H., Dick, A., Reid, I., & Schindler, K. (2017). Online multi-target tracking using recurrent neural networks. In Conference on artificial on intelligence.
– reference: Son, J., Baek, M., Cho, M., & Han, B. (2017). Multi-object tracking with quadruplet convolutional neural networks. In Conference on computer vision and pattern recognition.
– reference: BernardinKStiefelhagenREvaluating multiple object tracking performance: The CLEAR MOT metricsImage and Video Processing200810.1155/2008/246309
– reference: LeeS-HKimM-YBaeS-HLearning discriminative appearance models for online multi-object tracking with appearance discriminability measuresAccess20186673166732810.1109/ACCESS.2018.2879535
– reference: Tang, S., Andriluka, M., Andres, B., & Schiele, B. (2017). Multiple people tracking with lifted multicut and person re-identification. In Conference on computer vision and pattern recognition.
– reference: Yoon, J. H., Lee, C. R., Yang, M. H., & Yoon, K. J. (2016). Online multi-object tracking via structural constraint event aggregation. In International conference on computer vision and pattern recognition.
– reference: Yoon, Y.-C., Song, Y.-M., Yoon, K., & Jeon, M. (2018). Online multi-object tracking using selective deep appearance matching. In International conference on consumer electronics Asia.
– reference: Dave, A., Khurana, T., Tokmakov, P., Schmid, C., & Ramanan, D. (2020) Tao: A large-scale benchmark for tracking any object. In European conference on computer vision.
– reference: YoonKKimDYYoonY-CJeonMData association for multi-object tracking via deep neural networksSensors20191955910.3390/s19030559
– reference: Nguyen Thi Lan Anh, F. N., Khan, Furqan, & Bremond, F. (2017). Multi-object tracking using multi-channel part appearance representation. In International conference on advanced video and signal based surveillance.
– reference: SongYYoonKYoonYYowKJeonMOnline multi-object tracking with GMPHD filter and occlusion group managementAccess2019716510316512110.1109/ACCESS.2019.2953276
– reference: Taskar, B., Guestrin, C., & Koller, D. (2003). Max-margin Markov networks. In Advances in neural information processing systems.
– reference: Andriluka, M., Roth, S., & Schiele, B. (2010). Monocular 3D pose estimation and tracking by detection. In Conference on computer vision and pattern recognition.
– reference: Henschel, R., Leal-Taixé, L., Cremers, D., & Rosenhahn, B. (2018). Fusion of head and full-body detectors for multi-object tracking. In Conference on computer vision and pattern recognition workshops.
– reference: Dehghan, A., Assari, S. M., & Shah, M. (2015) GMMCP-tracker: Globally optimal generalized maximum multi clique problem for multiple object tracking. In Conference on computer vision and pattern recognition workshops.
– reference: ShengHZhangYChenJXiongZZhangJHeterogeneous association graph fusion for target association in multiple object trackingTransactions on Circuits and Systems for Video Technology201829113269328010.1109/TCSVT.2018.2882192
– reference: KarunasekeraHWangHZhangHMultiple object tracking with attention to appearance, structure, motion and sizeAccess201910.1109/ACCESS.2019.2932301
– reference: Ristani, E., Solera, F., Zou, R., Cucchiara, R., & Tomasi, C. (2016). Performance measures and a data set for multi-target, multi-camera tracking. In European conference on computer vision.
– reference: Manen, S., Timofte, R., Dai, D., & Gool, L. V. (2016). Leveraging single for multi-target tracking using a novel trajectory overlap affinity measure. In Winter conference on applications of computer vision.
– reference: Dendorfer, P., Rezatofighi, H., Milan, A., Shi, J., Cremers, D., Reid, I., Roth, S., Schindler, K., & Leal-Taixé, L. (2020). MOT20: A benchmark for multi object tracking in crowded scenes. arXiv preprint arXiv:2003.09003.
– reference: Smith, K., Gatica-Perez, D., Odobez, J.-M., & Ba, S. (2005). Evaluating multi-object tracking. In Workshop on empirical evaluation methods in computer vision.
– reference: BabaeeMLiZRigollGA dual CNN-RNN for multiple people trackingNeurocomputing2019368698310.1016/j.neucom.2019.08.008
– reference: Baisa, N. L. (2019a). Online multi-object visual tracking using a GM-PHD filter with deep appearance learning. In International conference on information fusion.
– reference: Kesten, R., Usman, M., Houston, J., Pandya, T., Nadhamuni, K., et al. (2019) Lyft level 5 av dataset 2019. https://level5.lyft.com/dataset/.
– reference: XiangJXuGMaCHouJEnd-to-end learning deep CRF models for multi-object trackingTransactions on Circuits and Systems for Video Technology202010.1109/TCSVT.2020.2975842
– reference: Sheng, H., Hao, L., Chen, J., et al. (2017). Robust local effective matching model for multi-target tracking. In Advances in multimedia information processing (Vol. 127, No. 8).
– reference: Zhu, J., Yang, H., Liu, N., Kim, M., Zhang, W., & Yang, M.-H. (2018). Online multi-object tracking with dual matching attention networks. In European conference on computer vision workshops.
– reference: Dalal, N., & Triggs, B. (2005). Histograms of oriented gradients for human detection. In Conference on computer vision and pattern recognition workshops.
– reference: WangSFowlkesCLearning optimal parameters for multi-target tracking with contextual interactionsInternational Journal of Computer Vision20161223484501363602210.1007/s11263-016-0960-z
– reference: Henschel, R., Zou, Y., & Rosenhahn, B. (2019). Multiple people tracking using body and joint detections. In Conference on computer vision and pattern recognition workshops.
– reference: Felzenszwalb, P. F., & Huttenlocher, D. P. (2006) Efficient belief propagation for early vision. In Conference on computer vision and pattern recognition.
– reference: Henriques, J. a., Caseiro, R., & Batista, J. (2011). Globally optimal solution to multi-object tracking with merged measurements. In International conference on computer vision.
– reference: ShiXLingHPangYYHuWChuPXingJRank-1 tensor approximation for high-order association in multi-target trackingInternational Journal of Computer Vision201812710631083397760010.1007/s11263-018-01147-z
– reference: Baisa, N. L. (2018). Online multi-target visual tracking using a HISP filter. In International joint conference on computer vision, imaging and computer graphics theory and applications.
– reference: Bochinski, E., Eiselein, V., & Sikora, T. (2017). High-speed tracking-by-detection without using image information. In International conference on advanced video and signal based surveillance.
– reference: ReidDBAn algorithm for tracking multiple targetsTransactions on Automatic Control197924684385410.1109/TAC.1979.1102177
– reference: FuZAngeliniFChambersJNaqviSMMulti-level cooperative fusion of GM-PHD filters for online multiple human trackingTransactions on Multimedia20192192277229110.1109/TMM.2019.2902480
– reference: YoonKGwakJSongYYoonYJeonMOneShotDa: Online multi-object tracker with one-shot-learning-based data associationAccess20208380603807210.1109/ACCESS.2020.2975912
– reference: Dendorfer, P., Rezatofighi, H., Milan, A., Shi, J., Cremers, D., Reid, I., Roth, S., Schindler, K., & Leal-Taixe, L. (2019). Cvpr19 tracking and detection challenge: How crowded can it get? arXiv preprint arXiv:1906.04567.
– reference: GeigerALauerMWojekCStillerCUrtasunR3D traffic scene understanding from movable platformsTransactions on Pattern Analysis and Machine Intelligence20143651012102510.1109/TPAMI.2013.185
– reference: TianWLauerMChenLOnline multi-object tracking using joint domain information in traffic scenariosTransactions on Intelligent Transportation Systems201921137438410.1109/TITS.2019.2892413
– reference: Sun, P., Kretzschmar, H., Dotiwalla, X., Chouard, A., Patnaik, V., Tsui, P., Guo, J., Zhou, Y., Chai, Y., & Caine, B., et al. (2020). Scalability in perception for autonomous driving: Waymo open dataset. In Conference on computer vision and pattern recognition.
– reference: ZhangYShengHWuYWangSLyuWKeWLong-term tracking with deep tracklet associationTransactions on Image Processing2020296694670610.1109/TIP.2020.2993073
– reference: Dicle, C., Camps, O., & Sznaier, M. (2013) The way they move: Tracking targets with similar appearance. In International conference on computer vision.
– reference: Seitz, S. M., Curless, B., Diebel, J., Scharstein, D., & Szeliski, R. (2006). A comparison and evaluation of multi-view stereo reconstruction algorithms. In Conference on computer vision and pattern recognition.
– reference: Wojke, N., & Paulus, D. (2016). Global data association for the probability hypothesis density filter using network flows. International conference on robotics and automation.
– reference: Kristan, M., et al. (2014). The visual object tracking VOT2014 challenge results. In European conference on computer vision workshops.
– reference: LeeSKimEMultiple object tracking via feature pyramid Siamese networksAccess201978181819410.1109/ACCESS.2018.2889442
– reference: Levinkov, E., Uhrig, J., Tang, S., Omran, M., Insafutdinov, E., Kirillov, A., Rother, C., Brox, T., Schiele, B., & Andres, B. (2017). Joint graph decomposition and node labeling: Problem, algorithms, applications. In Conference on computer vision and pattern recognition.
– reference: KeuperMTangSAndresBBroxTSchieleBMotion segmentation and multiple object tracking by correlation co-clusteringTransactions on Pattern Analysis and Machine Intelligence201810.1109/TPAMI.2018.2876253
– reference: Ren, S., He, K., Girshick, R., & Sun, J. (2015). Faster R-CNN: Towards real-time object detection with region proposal networks. In Advances in neural information processing systems.
– reference: Chu, P., & Ling, H. (2019). FAMNet: Joint learning of feature, affinity and multi-dimensional assignment for online multiple object tracking. In International conference on computer vision.
– reference: Hadsell, R., Chopra, S., & LeCun, Y. (2006). Dimensionality reduction by learning an invariant mapping. In Conference on computer vision and pattern recognition.
– reference: Mathias, M., Benenson, R., Pedersoli, M., & Gool, L. V. (2014). Face detection without bells and whistles. In European conference on computer vision workshops.
– reference: LiuQLiuBWuYLiWYuNReal-time online multi-object tracking in compressed domainAccess20197764897649910.1109/ACCESS.2019.2921975
– reference: BakerSScharsteinDLewisJPRothSBlackMJSzeliskiRA database and evaluation methodology for optical flowInternational Journal of Computer Vision201192113110.1007/s11263-010-0390-2
– reference: Li, B., Yan, J., Wu, W., Zhu, Z., & Hu, X. (2018). High performance visual tracking with Siamese region proposal network. In Conference on computer vision and pattern recognition.
– reference: Chu, Q., Ouyang, W., Li, H., Wang, X., Liu, B., & Yu, N. (2017). Online multi-object tracking using CNN-based single object tracker with spatial-temporal attention mechanism. In International conference on computer vision.
– reference: Held, D., Thrun, S., & Savarese, S. (2016). Learning to track at 100 fps with deep regression networks. In European conference on computer vision.
– reference: JuJKimDKuBHanDKKoHOnline multi-person tracking with two-stage data association and online appearance model learningIET Computer Vision2017111879510.1049/iet-cvi.2016.0068
– reference: Maksai, A., & Fua, P. (2019). Eliminating exposure bias and metric mismatch in multiple object tracking. In Conference on computer vision and pattern recognition.
– reference: Xu, J., Cao, Y., Zhang, Z., & Hu, H. (2019). Spatial-temporal relation networks for multi-object tracking. In International conference on computer vision.
– reference: FuZFengPAngeliniFChambersJANaqviSMParticle PHD filter based multiple human tracking using online group-structured dictionary learningAccess20186147641477810.1109/ACCESS.2018.2816805
– reference: Leal-Taixé, L., Fenzi, M., Kuznetsova, A., Rosenhahn, B., & Savarese, S. (2014). Learning an image-based motion context for multiple people tracking. In Conference on computer vision and pattern recognition.
– reference: Chen, W., Chen, X., Zhang, J., & Huang, K. (2017b). Beyond triplet loss: A deep quadruplet network for person re-identification. In Conference on computer vision and pattern recognition.
– reference: JuJKimDKuBHanDKoHOnline multi-object tracking with efficient track drift and fragmentation handlingJournal of the Optical Society of America A201734228029310.1364/JOSAA.34.000280
– reference: Baisa, N. L. (2019b). Occlusion-robust online multi-object visual tracking using a GM-PHD filter with a CNN-based re-identification. arXiv preprint arXiv:1912.05949.
– reference: Zamir, A. R., Dehghan, A., & Shah, M. (2012). GMCP-Tracker: Global multi-object tracking using generalized minimum clique graphs. In European conference on computer vision.
– reference: Choi, W. (2015). Near-online multi-target tracking with aggregated local flow descriptor. In International conference on computer vision.
– reference: Alahi, A., Ramanathan, V., & Fei-Fei, L. (2014). Socially-aware large-scale crowd forecasting. In Conference on computer vision and pattern recognition.
– reference: SchuhmacherDVoB-TVoB-NA consistent metric for performance evaluation of multi-object filtersTransactions on Signal Processing200856834473457251695510.1109/TSP.2008.920469
– reference: BaisaNLWallaceADevelopment of a n-type GM-PHD filter for multiple target, multiple type visual trackingJournal of Visual Communication and Image Representation20195925727110.1016/j.jvcir.2019.01.026
– reference: ThrunSBurgardWFoxDProbabilistic robotics (intelligent robotics and autonomous agents)2005CambridgeThe MIT Press1081.68703
– reference: Kieritz, H., Becker, S., Häbner, W., & Arens, M. (2016). Online multi-person tracking using integral channel features. In International conference on advanced video and signal based surveillance.
– reference: WenLDuDCaiZLeiZChangMQiHUA-DETRAC: A new benchmark and protocol for multi-object detection and trackingComputer Vision and Image Understanding202019310290710.1016/j.cviu.2020.102907
– reference: Boragule, A., & Jeon, M. (2017). Joint cost minimization for multi-object tracking. International conference on advanced video and signal based surveillance.
– reference: Pirsiavash, H., Ramanan, D., & Fowlkes, C. C. (2011). Globally-optimal greedy algorithms for tracking a variable number of objects. In Conference on computer vision and pattern recognition.
– reference: Benfold, B., & Reid, I. (2011). Unsupervised learning of a scene-specific coarse gaze estimator. In International conference on computer vision.
– reference: Geiger, A., Lenz, P., & Urtasun, R. (2012) Are we ready for autonomous driving? The KITTI vision benchmark suite. In Conference on computer vision and pattern recognition.
– reference: Tang, S., Andres, B., Andriluka, M., & Schiele, B. (2015). Subgraph decomposition for multi-target tracking. In Conference on computer vision and pattern recognition.
– reference: Kim, C., Li, F., & Rehg, J. M. (2018). Multi-object tracking with neural gating using bilinear LSTM. In European conference on computer vision.
– reference: Milan, A., Schindler, K., & Roth, S. (2013). Challenges of ground truth evaluation of multi-target tracking. In Conference on computer vision and pattern recognition workshops.
– reference: EveringhamMEslamiSAVan GoolLWilliamsCKWinnJZissermanAThe Pascal visual object classes challenge: A retrospectiveInternational Journal of Computer Vision201511119813610.1007/s11263-014-0733-5
– reference: RussakovskyODengJSuHKrauseJSatheeshSMaSImageNet large scale visual recognition challengeInternational Journal of Computer Vision20151153211252342248210.1007/s11263-015-0816-y
– reference: Kim, C., Li, F., Ciptadi, A., & Rehg, J. M. (2015). Multiple hypothesis tracking revisited. In International conference on computer vision.
– reference: Wang, G., Wang, Y., Zhang, H., Gu, R., & Hwang, J.-N. (2019). Exploit the connectivity: Multi-object tracking with trackletnet. In International conference on multimedia.
– reference: YangMWuYJiaYA hybrid data association framework for robust online multi-object trackingTransactions on Image Processing201710.1109/TIP.2017.27451031409.94704
– reference: Yoon, Y., Kim, D. Y., Yoon, K., Song, Y., & Jeon, M. (2019b). Online multiple pedestrian tracking using deep temporal appearance matching association. arXiv preprint arXiv:1907.00831.
– reference: WuHHuYWangKLiHNieLChengHInstance-aware representation learning and association for online multi-person trackingPattern Recognition201994253410.1016/j.patcog.2019.04.018
– reference: Tao, Y., Chen, J., Fang, Y., Masaki, I., & Horn, B. K. (2018). Adaptive spatio-temporal model based multiple object tracking in video sequences considering a moving camera. In International conference on universal village.
– reference: Xu, Y., Osep, A., Ban, Y., Horaud, R., Leal-Taixe, L., & Alameda-Pineda, X. (2020). How to train your deep multi-object tracker. In Conference on computer vision and pattern recognition.
– reference: Li, Y., Huang, C., & Nevatia, R. (2009). Learning to associate: Hybrid boosted multi-target tracker for crowded scene. In Conference on computer vision and pattern recognition.
– reference: Wu, B., & Nevatia, R. (2006). Tracking of multiple, partially occluded humans based on static body part detection. In Conference on computer vision and pattern recognition.
– reference: Baisa, N. L. (2019c). Robust online multi-target visual tracking using a HISP filter with discriminative deep appearance learning. arXiv preprint arXiv:1908.03945.
– reference: LanLWangXZhangSTaoDGaoWHuangTSInteracting tracklets for multi-object trackingTransactions on Image Processing201827945854597381868210.1109/TIP.2018.2843129
– reference: Torralba, A., & Efros, A. A. (2011). Unbiased look at dataset bias. In Conference on computer vision and pattern recognition.
– reference: Bewley, A., Ge, Z., Ott, L., Ramos, F., & Upcroft, B. (2016a). Simple online and realtime tracking. In International conference on image processing.
– reference: Ferryman, J., & Shahrokni, A. (2009). PETS2009: Dataset and challenge. In International workshop on performance evaluation of tracking and surveillance.
– reference: KuhnHWYawBThe Hungarian method for the assignment problemNaval Research Logistics Quarterly1955283977551010.1002/nav.3800020109
– reference: Song, Y., Yoon, Y., Yoon, K., & Jeon, M. (2018). Online and real-time tracking with the GMPHD filter using group management and relative motion analysis. In International conference on advanced video and signal based surveillance.
– reference: YangMJiaYTemporal dynamic appearance modeling for online multi-person trackingComputer Vision and Image Understanding201610.1016/j.cviu.2016.05.003
– reference: Fang, K., Xiang, Y., Li, X., & Savarese, S. (2018). Recurrent autoregressive networks for online multi-object tracking. In Winter conference on applications of computer vision.
– reference: Andriluka, M., Iqbal, U., Insafutdinov, E., Pishchulin, L., Milan, A., Gall, J., & Schiele, B. (2018). Posetrack: A benchmark for human pose estimation and tracking. In Conference on computer vision and pattern recognition.
– reference: Ma, C., Yang, C., Yang, F., Zhuang, Y., Zhang, Z., Jia, H., & Xie, X. (2018a). Trajectory factory: Tracklet cleaving and re-connection by deep Siamese bi-GRU for multiple object tracking. In International conference on multimedia and expo.
– reference: Zhang, L., Li, Y., & Nevatia, R. (2008). Global data association for multi-object tracking using network flows. In Conference on computer vision and pattern recognition.
– reference: Dollár, P., Wojek, C., Schiele, B., & Perona, P. (2009) Pedestrian detection: A benchmark. In Conference on computer vision and pattern recognition workshops.
– reference: Long, C., Haizhou, A., Zijie, Z., & Chong, S. (2018) Real-time multiple people tracking with deeply learned candidate selection and person re-identification. In International conference on multimedia and expo.
– reference: Brasó, G., & Leal-Taixé, L. (2020). Learning a neural solver for multiple object tracking. In Conference on computer vision and pattern recognition.
– reference: Kutschbach, T., Bochinski, E., Eiselein, V., & Sikora, T. (2017). Sequential sensor fusion combining probability hypothesis density and kernelized correlation filters for multi-object tracking in video data. In International conference on advanced video and signal based surveillance.
– reference: Song, Y., & Jeon, M. (2016). Online multiple object tracking with the hierarchically adopted GM-PHD filter using motion and appearance. In International conference on consumer electronics.
– reference: Long, C., Haizhou, A., Chong, S., Zijie, Z., & Bo, B. (2017). Online multi-object tracking with convolutional neural networks. In International conference on image processing.
– reference: Fagot-Bouquet, L., Audigier, R., Dhome, Y., & Lerasle, F. (2016). Improving multi-frame data association with sparse representations for robust near-online multi-object tracking. In European conference on computer vision workshops.
– reference: Loumponias, K., Dimou, A., Vretos, N., & Daras, P. (2018). Adaptive tobit Kalman-based tracking. In International conference on signal-image technology & internet-based systems.
– reference: ShengHChenJZhangYKeWXiongZYuJIterative multiple hypothesis tracking with tracklet-level associationTransactions on Circuits and Systems for Video Technology201829123660367210.1109/TCSVT.2018.2881123
– reference: Leal-Taixe, L., Canton-Ferrer, C., & Schindler, K. (2016). Learning by tracking: Siamese CNN for robust target association. In Conference on computer vision and pattern recognition workshops.
– reference: Pedersen, M., Haurum, J. B., Bengtson, S. H., & Moeslund, T. B. (June 2020). 3D-ZEF: A 3D zebrafish tracking benchmark dataset. In Conference on computer vision and pattern recognition.
– reference: Fagot-Bouquet, L., Audigier, R., Dhome, Y., & Lerasle, F. (2015). Online multi-person tracking based on global sparse collaborative representations. In International conference on image processing.
– reference: Wen, L., Li, W., Yan, J., Lei, Z., Yi, D., & Li, S. Z. (2014). Multiple target tracking based on undirected hierarchical relation hypergraph. In Conference on computer vision and pattern recognition.
– reference: Milan, A., Leal-Taixé, L., Schindler, K., & Reid, I. (2015). Joint tracking and segmentation of multiple targets. In Conference on computer vision and pattern recognition.
– reference: Xiang, Y., Alahi, A., & Savarese, S. (2015). Learning to track: Online multi-object tracking by decision making. In International conference on computer vision.
– reference: ZhouHOuyangWChengJWangXLiHDeep continuous conditional random fields with asymmetric inter-object constraints for online multi-object trackingTransactions on Circuits and Systems for Video Technology201810.1109/TCSVT.2018.2825679
– reference: Chen, J., Sheng, H., Zhang, Y., & Xiong, Z. (2017a). Enhancing detection model for multiple hypothesis tracking. In Conference on computer vision and pattern recognition workshops.
– reference: Yang, F., Choi, W., & Lin, Y. (2016). Exploit all the layers: Fast and accurate CNN object detector with scale dependent pooling and cascaded rejection classifiers. In Conference on computer vision and pattern recognition.
– reference: Chu, P., Fan, H., Tan, C. C., & Ling, H. (2019). Online multi-object tracking with instance-aware tracker and dynamic model refreshment. In Winter conference on applications of computer vision.
– reference: ChenLAiHChenRZhuangZAggregate tracklet appearance features for multi-object trackingSignal Processing Letters201926111613161710.1109/LSP.2019.2940922
– reference: Tang, S., Andres, B., Andriluka, M., & Schiele, B. (2016). Multi-person tracking by multicuts and deep matching. In European conference on computer vision workshops.
– reference: Bae, S.-H., & Yoon, K.-J. (2014). Robust online multi-object tracking based on tracklet confidence and online discriminative appearance learning. In Conference on computer vision and pattern recognition.
– reference: Bergmann, P., Meinhardt, T., & Leal-Taixé, L. (2019). Tracking without bells and whistles. In International conference on computer vision.
– reference: Girshick, R. (2015). Fast R-CNN. In International conference on computer vision.
– reference: Yoon, J., Yang, H., Lim, J., & Yoon, K. (2015). Bayesian multi-object tracking using motion context from multiple objects. In Winter conference on applications of computer vision.
– reference: MilanASchindlerKRothSMulti-target tracking by discrete-continuous energy minimizationTransactions on Pattern Analysis and Machine Intelligence201638102054206810.1109/TPAMI.2015.2505309
– reference: Rezatofighi, H., Milan, A., Zhang, Z., Shi, Q., Dick, A., & Reid, I. (2015). Joint probabilistic data association revisited. In International conference on computer vision.
– reference: Sanchez-Matilla, R., Poiesi, F., & Cavallaro, A. (2016). Online multi-target tracking with strong and weak detections. In European conference on computer vision workshops.
– ident: 1393_CR31
  doi: 10.1109/CVPR.2005.177
– ident: 1393_CR88
  doi: 10.1109/WACV.2016.7477566
– ident: 1393_CR104
  doi: 10.1109/ICCV.2017.41
– ident: 1393_CR20
  doi: 10.1109/AVSS.2017.8078516
– year: 2018
  ident: 1393_CR63
  publication-title: Transactions on Pattern Analysis and Machine Intelligence
  doi: 10.1109/TPAMI.2018.2876253
– ident: 1393_CR94
  doi: 10.1109/CVPRW.2013.111
– year: 2017
  ident: 1393_CR144
  publication-title: Transactions on Image Processing
  doi: 10.1109/TIP.2017.2745103
– ident: 1393_CR96
  doi: 10.1109/AVSS.2017.8078552
– ident: 1393_CR42
  doi: 10.1109/ICIP.2015.7351235
– volume: 34
  start-page: 280
  issue: 2
  year: 2017
  ident: 1393_CR59
  publication-title: Journal of the Optical Society of America A
  doi: 10.1364/JOSAA.34.000280
– ident: 1393_CR53
  doi: 10.1109/CVPR.2006.100
– ident: 1393_CR39
  doi: 10.1109/AVSS.2012.59
– ident: 1393_CR136
– ident: 1393_CR125
  doi: 10.1109/UV.2018.8642156
– ident: 1393_CR71
  doi: 10.1007/978-3-319-48881-3_4
– ident: 1393_CR91
  doi: 10.1109/CVPR.2015.7299178
– ident: 1393_CR124
  doi: 10.1109/CVPR.2017.394
– ident: 1393_CR56
  doi: 10.1109/CVPRW.2018.00192
– ident: 1393_CR51
  doi: 10.1109/CVPR.2012.6248074
– ident: 1393_CR24
  doi: 10.1109/CVPRW.2017.266
– ident: 1393_CR78
  doi: 10.1109/CVPR.2018.00935
– ident: 1393_CR98
  doi: 10.1109/CVPR.2011.5995604
– ident: 1393_CR139
  doi: 10.1109/ICCV.2015.534
– volume: 19
  start-page: 559
  year: 2019
  ident: 1393_CR148
  publication-title: Sensors
  doi: 10.3390/s19030559
– volume: 38
  start-page: 2054
  issue: 10
  year: 2016
  ident: 1393_CR95
  publication-title: Transactions on Pattern Analysis and Machine Intelligence
  doi: 10.1109/TPAMI.2015.2505309
– volume: 122
  start-page: 484
  issue: 3
  year: 2016
  ident: 1393_CR132
  publication-title: International Journal of Computer Vision
  doi: 10.1007/s11263-016-0960-z
– ident: 1393_CR64
  doi: 10.1109/AVSS.2016.7738059
– volume: 11
  start-page: 87
  issue: 1
  year: 2017
  ident: 1393_CR60
  publication-title: IET Computer Vision
  doi: 10.1049/iet-cvi.2016.0068
– ident: 1393_CR19
  doi: 10.1109/ICRA.2016.7487371
– ident: 1393_CR32
  doi: 10.1007/978-3-030-58558-7_26
– volume: 7
  start-page: 76489
  year: 2019
  ident: 1393_CR80
  publication-title: Access
  doi: 10.1109/ACCESS.2019.2921975
– ident: 1393_CR45
  doi: 10.1007/s11263-006-7899-4
– ident: 1393_CR26
  doi: 10.1109/CVPR.2017.145
– ident: 1393_CR79
– ident: 1393_CR54
  doi: 10.1007/978-3-319-46448-0_45
– volume: 111
  start-page: 98
  issue: 1
  year: 2015
  ident: 1393_CR41
  publication-title: International Journal of Computer Vision
  doi: 10.1007/s11263-014-0733-5
– volume: 8
  start-page: 38060
  year: 2020
  ident: 1393_CR147
  publication-title: Access
  doi: 10.1109/ACCESS.2020.2975912
– volume: 92
  start-page: 1
  issue: 1
  year: 2011
  ident: 1393_CR12
  publication-title: International Journal of Computer Vision
  doi: 10.1007/s11263-010-0390-2
– ident: 1393_CR62
– ident: 1393_CR66
  doi: 10.1007/978-3-030-01237-3_13
– ident: 1393_CR142
  doi: 10.1109/CVPR.2016.234
– volume: 29
  start-page: 3660
  issue: 12
  year: 2018
  ident: 1393_CR110
  publication-title: Transactions on Circuits and Systems for Video Technology
  doi: 10.1109/TCSVT.2018.2881123
– ident: 1393_CR30
  doi: 10.1109/ICCV.2017.518
– volume: 7
  start-page: 8181
  year: 2019
  ident: 1393_CR75
  publication-title: Access
  doi: 10.1109/ACCESS.2018.2889442
– ident: 1393_CR102
  doi: 10.1007/978-3-319-48881-3_2
– ident: 1393_CR22
  doi: 10.1109/CVPR42600.2020.00628
– ident: 1393_CR140
  doi: 10.1109/ICCV.2019.00409
– ident: 1393_CR7
  doi: 10.5220/0006564504290438
– ident: 1393_CR111
– ident: 1393_CR34
– ident: 1393_CR82
– ident: 1393_CR55
  doi: 10.1109/ICCV.2011.6126532
– ident: 1393_CR105
  doi: 10.1109/ICIP.2019.8803140
– year: 2016
  ident: 1393_CR143
  publication-title: Computer Vision and Image Understanding
  doi: 10.1016/j.cviu.2016.05.003
– ident: 1393_CR120
– volume: 47
  start-page: 7
  issue: 1
  year: 2002
  ident: 1393_CR107
  publication-title: International Journal of Computer Vision
  doi: 10.1023/A:1014573219977
– ident: 1393_CR67
– ident: 1393_CR73
  doi: 10.1109/CVPR.2014.453
– ident: 1393_CR100
– ident: 1393_CR153
  doi: 10.1109/CVPR.2008.4587584
– ident: 1393_CR9
  doi: 10.5220/0006564504290438
– ident: 1393_CR43
  doi: 10.1007/978-3-319-46484-8_47
– ident: 1393_CR129
  doi: 10.1109/CVPR.2011.5995347
– ident: 1393_CR29
  doi: 10.1109/ICCV.2019.00627
– ident: 1393_CR77
  doi: 10.1109/CVPR.2017.206
– volume-title: Probabilistic robotics (intelligent robotics and autonomous agents)
  year: 2005
  ident: 1393_CR127
– year: 2020
  ident: 1393_CR138
  publication-title: Transactions on Circuits and Systems for Video Technology
  doi: 10.1109/TCSVT.2020.2975842
– volume: 368
  start-page: 69
  year: 2019
  ident: 1393_CR4
  publication-title: Neurocomputing
  doi: 10.1016/j.neucom.2019.08.008
– ident: 1393_CR23
  doi: 10.1109/CVPR.2019.00895
– ident: 1393_CR40
  doi: 10.1109/CVPR.2008.4587581
– ident: 1393_CR1
  doi: 10.1109/CVPR.2014.283
– volume: 8
  start-page: 217
  issue: 11
  year: 2017
  ident: 1393_CR86
  publication-title: International Journal of Advanced Computer Science & Applications
  doi: 10.14569/IJACSA.2017.081129
– ident: 1393_CR89
  doi: 10.1007/978-3-319-10593-2_47
– ident: 1393_CR145
  doi: 10.1109/WACV.2015.12
– ident: 1393_CR149
  doi: 10.1109/AVSS.2018.8639078
– ident: 1393_CR92
  doi: 10.1609/aaai.v31i1.11194
– ident: 1393_CR134
  doi: 10.1109/CVPR.2014.167
– volume: 36
  start-page: 1532
  issue: 8
  year: 2014
  ident: 1393_CR37
  publication-title: Transactions on Pattern Analysis and Machine Intelligence
  doi: 10.1109/TPAMI.2014.2300479
– ident: 1393_CR87
  doi: 10.1109/CVPR.2019.00477
– volume: 6
  start-page: 67316
  year: 2018
  ident: 1393_CR76
  publication-title: Access
  doi: 10.1109/ACCESS.2018.2879535
– ident: 1393_CR14
– ident: 1393_CR156
– volume: 21
  start-page: 374
  issue: 1
  year: 2019
  ident: 1393_CR128
  publication-title: Transactions on Intelligent Transportation Systems
  doi: 10.1109/TITS.2019.2892413
– ident: 1393_CR135
  doi: 10.1109/ICRA.2016.7487180
– year: 2018
  ident: 1393_CR155
  publication-title: Transactions on Circuits and Systems for Video Technology
  doi: 10.1109/TCSVT.2018.2825679
– ident: 1393_CR52
  doi: 10.1109/ICCV.2015.169
– ident: 1393_CR157
  doi: 10.1007/978-3-030-01228-1_23
– ident: 1393_CR90
  doi: 10.1109/WACV.2015.17
– ident: 1393_CR130
  doi: 10.1109/CVPRW.2016.55
– ident: 1393_CR21
  doi: 10.1109/AVSS.2017.8078481
– ident: 1393_CR122
  doi: 10.1109/CVPR.2015.7299138
– ident: 1393_CR5
  doi: 10.1109/CVPR.2014.159
– ident: 1393_CR16
  doi: 10.1109/ICCV.2019.00103
– ident: 1393_CR3
  doi: 10.1109/CVPR.2018.00542
– ident: 1393_CR126
– ident: 1393_CR15
  doi: 10.1109/ICCV.2011.6126516
– year: 2019
  ident: 1393_CR61
  publication-title: Access
  doi: 10.1109/ACCESS.2019.2932301
– volume: 29
  start-page: 3269
  issue: 11
  year: 2018
  ident: 1393_CR113
  publication-title: Transactions on Circuits and Systems for Video Technology
  doi: 10.1109/TCSVT.2018.2882192
– ident: 1393_CR115
  doi: 10.1109/CVPR.2005.453
– ident: 1393_CR131
  doi: 10.1145/3343031.3350853
– volume: 27
  start-page: 4585
  issue: 9
  year: 2018
  ident: 1393_CR70
  publication-title: Transactions on Image Processing
  doi: 10.1109/TIP.2018.2843129
– volume: 7
  start-page: 165103
  year: 2019
  ident: 1393_CR119
  publication-title: Access
  doi: 10.1109/ACCESS.2019.2953276
– ident: 1393_CR10
  doi: 10.5220/0006564504290438
– ident: 1393_CR38
  doi: 10.1109/CVPRW.2009.5206631
– ident: 1393_CR83
  doi: 10.1109/SITIS.2018.00021
– volume: 94
  start-page: 25
  year: 2019
  ident: 1393_CR137
  publication-title: Pattern Recognition
  doi: 10.1016/j.patcog.2019.04.018
– ident: 1393_CR36
  doi: 10.1109/ICCV.2013.286
– volume: 7
  start-page: 2107
  year: 2018
  ident: 1393_CR112
  publication-title: Access
  doi: 10.1109/ACCESS.2018.2881019
– ident: 1393_CR13
  doi: 10.1007/978-3-319-48881-3_5
– ident: 1393_CR101
  doi: 10.1109/ICCV.2015.349
– ident: 1393_CR47
  doi: 10.1109/PETS-WINTER.2009.5399556
– ident: 1393_CR146
  doi: 10.1109/CVPR.2016.155
– ident: 1393_CR116
  doi: 10.1109/CVPR.2017.403
– volume: 24
  start-page: 843
  issue: 6
  year: 1979
  ident: 1393_CR99
  publication-title: Transactions on Automatic Control
  doi: 10.1109/TAC.1979.1102177
– ident: 1393_CR150
– volume: 29
  start-page: 6694
  year: 2020
  ident: 1393_CR154
  publication-title: Transactions on Image Processing
  doi: 10.1109/TIP.2020.2993073
– volume: 59
  start-page: 257
  year: 2019
  ident: 1393_CR11
  publication-title: Journal of Visual Communication and Image Representation
  doi: 10.1016/j.jvcir.2019.01.026
– ident: 1393_CR46
  doi: 10.1109/AVSS.2010.90
– volume: 40
  start-page: 595
  issue: 3
  year: 2018
  ident: 1393_CR6
  publication-title: Transactions on Pattern Analysis and Machine Intelligence
  doi: 10.1109/TPAMI.2017.2691769
– volume: 6
  start-page: 14764
  year: 2018
  ident: 1393_CR49
  publication-title: Access
  doi: 10.1109/ACCESS.2018.2816805
– ident: 1393_CR33
  doi: 10.1109/CVPR.2015.7299036
– volume: 36
  start-page: 58
  issue: 1
  year: 2014
  ident: 1393_CR93
  publication-title: Transactions on Pattern Analysis and Machine Intelligence
  doi: 10.1109/TPAMI.2013.103
– ident: 1393_CR74
  doi: 10.1109/ICCVW.2011.6130233
– ident: 1393_CR58
– ident: 1393_CR81
– ident: 1393_CR72
  doi: 10.1109/CVPRW.2016.59
– volume: 2
  start-page: 83
  year: 1955
  ident: 1393_CR68
  publication-title: Naval Research Logistics Quarterly
  doi: 10.1002/nav.3800020109
– volume: 21
  start-page: 2277
  issue: 9
  year: 2019
  ident: 1393_CR48
  publication-title: Transactions on Multimedia
  doi: 10.1109/TMM.2019.2902480
– ident: 1393_CR106
  doi: 10.1007/978-3-319-48881-3_7
– ident: 1393_CR65
  doi: 10.1109/ICCV.2015.533
– ident: 1393_CR44
  doi: 10.1109/WACV.2018.00057
– ident: 1393_CR118
– ident: 1393_CR8
  doi: 10.5220/0006564504290438
– ident: 1393_CR121
  doi: 10.1109/CVPR42600.2020.00252
– volume: 115
  start-page: 211
  issue: 3
  year: 2015
  ident: 1393_CR103
  publication-title: International Journal of Computer Vision
  doi: 10.1007/s11263-015-0816-y
– year: 2008
  ident: 1393_CR17
  publication-title: Image and Video Processing
  doi: 10.1155/2008/246309
– volume: 127
  start-page: 1063
  year: 2018
  ident: 1393_CR114
  publication-title: International Journal of Computer Vision
  doi: 10.1007/s11263-018-01147-z
– ident: 1393_CR2
  doi: 10.1109/CVPR.2010.5540156
– ident: 1393_CR84
  doi: 10.1109/ICME.2018.8486454
– volume: 26
  start-page: 1613
  issue: 11
  year: 2019
  ident: 1393_CR25
  publication-title: Signal Processing Letters
  doi: 10.1109/LSP.2019.2940922
– volume: 193
  start-page: 102907
  year: 2020
  ident: 1393_CR133
  publication-title: Computer Vision and Image Understanding
  doi: 10.1016/j.cviu.2020.102907
– ident: 1393_CR18
  doi: 10.1109/ICIP.2016.7533003
– volume: 36
  start-page: 1012
  issue: 5
  year: 2014
  ident: 1393_CR50
  publication-title: Transactions on Pattern Analysis and Machine Intelligence
  doi: 10.1109/TPAMI.2013.185
– ident: 1393_CR85
  doi: 10.1007/978-3-030-20890-5_39
– ident: 1393_CR117
  doi: 10.1109/ICCE-Asia.2016.7804800
– ident: 1393_CR152
– ident: 1393_CR69
  doi: 10.1109/AVSS.2017.8078517
– ident: 1393_CR109
  doi: 10.1109/CVPR.2006.19
– ident: 1393_CR141
  doi: 10.1109/CVPR42600.2020.00682
– ident: 1393_CR28
  doi: 10.1109/WACV.2019.00023
– ident: 1393_CR151
  doi: 10.1109/ICCE-ASIA.2018.8552105
– ident: 1393_CR97
  doi: 10.1109/CVPR42600.2020.00250
– ident: 1393_CR27
  doi: 10.1109/ICCV.2015.347
– ident: 1393_CR35
– volume: 56
  start-page: 3447
  issue: 8
  year: 2008
  ident: 1393_CR108
  publication-title: Transactions on Signal Processing
  doi: 10.1109/TSP.2008.920469
– ident: 1393_CR123
  doi: 10.1007/978-3-319-48881-3_8
– ident: 1393_CR57
  doi: 10.1109/CVPRW.2019.00105
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SubjectTerms Algorithms
Artificial Intelligence
Autonomous cars
Benchmarks
Cameras
Computer Imaging
Computer Science
Computer vision
Error analysis
Image Processing and Computer Vision
Labels
Machine learning
Machine vision
Multiple target tracking
Pattern Recognition
Pattern Recognition and Graphics
Pedestrians
Performance evaluation
Special Issue on Performance Evaluation in Computer Vision
Visibility
Vision
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