Click on a measure to sort the table accordingly. See below for a more detailed description.
Tracker | Avg Rank | MOTA | IDF1 | MT | ML | FP | FN | ID Sw. | Frag | Hz | Detector |
JPDA_OP 1. | 44.5 | 3.6 ±11.3 | 7.5 | 0.4% | 96.1% | 1,024 | 58,189 | 29 (5.5) | 119 (22.5) | 77.7 | Public |
Anonymous submission | |||||||||||
TO 2. | 44.9 | 25.7 ±13.5 | 32.7 | 4.3% | 57.4% | 4,779 | 40,511 | 383 (11.2) | 600 (17.6) | 5.0 | Public |
S. Manen, R. Timofte, D. Dai, L. Gool. Leveraging single for multi-target tracking using a novel trajectory overlap affinity measure. In 2016 IEEE Winter Conference on Applications of Computer Vision (WACV), 2016. | |||||||||||
UN_DAM 3. | 33.3 | 29.7 ±12.3 | 41.4 | 9.2% | 49.9% | 7,610 | 35,269 | 318 (7.5) | 674 (15.8) | 20.7 | Public |
Multi Object Tracking using Deep Structural Cost Minimization in Data Association | |||||||||||
SegTrack 4. | 53.1 | 22.5 ±15.2 | 31.5 | 5.8% | 63.9% | 7,890 | 39,020 | 697 (19.1) | 737 (20.2) | 0.2 | Public |
A. Milan, L. Leal-Taixé, K. Schindler, I. Reid. Joint Tracking and Segmentation of Multiple Targets. In CVPR, 2015. | |||||||||||
LINF1 5. | 39.8 | 24.5 ±15.4 | 34.8 | 5.5% | 64.6% | 5,864 | 40,207 | 298 (8.6) | 744 (21.5) | 7.5 | Public |
L. Fagot-Bouquet, R. Audigier, Y. Dhome, F. Lerasle. Improving Multi-Frame Data Association with Sparse Representations for Robust Near-Online Multi-Object Tracking. In ECCV, 2016. | |||||||||||
HAM_SADF 6. | 36.1 | 25.2 ±13.9 | 37.8 | 5.7% | 58.3% | 7,330 | 38,275 | 357 (9.5) | 745 (19.8) | 18.7 | Public |
Y. Yoon, A. Boragule, Y. Song, K. Yoon, M. Jeon. Online Multi-Object Tracking with Historical Appearance Matching and Scene Adaptive Detection Filtering. In IEEE AVSS, 2018. | |||||||||||
DSA_MOT 7. | 29.5 | 29.4 ±12.9 | 41.2 | 9.2% | 50.2% | 7,705 | 35,364 | 329 (7.8) | 789 (18.6) | 9.6 | Public |
Anonymous submission | |||||||||||
TFMOT 8. | 45.7 | 23.8 ±11.9 | 32.3 | 4.9% | 62.0% | 4,533 | 41,873 | 404 (12.7) | 792 (24.9) | 11.3 | Public |
M. Abhijeet Boragule. Joint Cost Minimization for Multi-Object Tracking. In 2017 IEEE International Conference on Advanced Vide and Signale Based Surveillance, 2017. | |||||||||||
DCO_X 9. | 49.7 | 19.6 ±14.1 | 31.5 | 5.1% | 54.9% | 10,652 | 38,232 | 521 (13.8) | 819 (21.7) | 0.3 | Public |
A. Milan, K. Schindler, S. Roth. Multi-Target Tracking by Discrete-Continuous Energy Minimization. In IEEE PAMI, 2016. | |||||||||||
NOMT 10. | 26.7 | 33.7 ±16.2 | 44.6 | 12.2% | 44.0% | 7,762 | 32,547 | 442 (9.4) | 823 (17.5) | 11.5 | Public |
W. Choi. Near-Online Multi-target Tracking with Aggregated Local Flow Descriptor. In ICCV, 2015. | |||||||||||
Tracker | Avg Rank | MOTA | IDF1 | MT | ML | FP | FN | ID Sw. | Frag | Hz | Detector |
TC_SIAMESE 11. | 48.1 | 20.2 ±13.9 | 32.6 | 2.6% | 67.5% | 6,127 | 42,596 | 294 (9.6) | 825 (26.9) | 13.0 | Public |
Y. Yoon, Y. Song, K. Yoon, M. Jeon. Online Multiple-Object Tracking using Selective Deep Appearance Matching. In IEEE/IEIE The International Conference on Consumer Electronics (ICCE) Asia, 2018. | |||||||||||
MHT_DAM 12. | 30.0 | 32.4 ±15.6 | 45.3 | 16.0% | 43.8% | 9,064 | 32,060 | 435 (9.1) | 826 (17.3) | 0.7 | Public |
C. Kim, F. Li, A. Ciptadi, J. Rehg. Multiple Hypothesis Tracking Revisited. In ICCV, 2015. | |||||||||||
LP_SSVM 13. | 40.5 | 25.2 ±13.7 | 34.0 | 5.8% | 53.0% | 8,369 | 36,932 | 646 (16.2) | 849 (21.3) | 41.3 | Public |
S. Wang, C. Fowlkes. Learning Optimal Parameters for Multi-target Tracking with Contextual Interactions. In International Journal of Computer Vision, 2016. | |||||||||||
MHT__ReID 14. | 28.8 | 33.0 ±15.1 | 46.4 | 17.6% | 42.6% | 8,725 | 32,046 | 421 (8.8) | 851 (17.8) | 0.3 | Public |
Anonymous submission | |||||||||||
JPDA_m 15. | 39.4 | 23.8 ±15.1 | 33.8 | 5.0% | 58.1% | 6,373 | 40,084 | 365 (10.5) | 869 (25.0) | 32.6 | Public |
H. Rezatofighi, A. Milan, Z. Zhang, Q. Shi, A. Dick, I. Reid. Joint Probabilistic Data Association Revisited. In ICCV, 2015. | |||||||||||
SLTV15 16. | 35.8 | 27.6 ±15.1 | 40.3 | 7.2% | 51.9% | 6,581 | 37,566 | 358 (9.2) | 884 (22.7) | 20.9 | Public |
Gwangju Institute of Science and Technology(GIST), Machine Learning and Vision Laboratory | |||||||||||
CNNTCM 17. | 34.0 | 29.6 ±13.9 | 36.8 | 11.2% | 44.0% | 7,786 | 34,733 | 712 (16.4) | 943 (21.7) | 1.7 | Public |
B. Wang, K. L. Chan, L. Wang, B. Shuai, Z. Zuo, T. Liu, G. Wang. Joint Learning of Convolutional Neural Networks and Temporally Constrained Metrics for Tracklet Association. In DeepVision Workshop (CVPR), 2016. | |||||||||||
TSMLCDEnew 18. | 29.9 | 34.3 ±13.1 | 44.1 | 14.0% | 39.4% | 7,869 | 31,908 | 618 (12.9) | 959 (20.0) | 6.5 | Public |
B. Wang, G. Wang, K. L. Chan, L. Wang. Tracklet Association by Online Target-Specific Metric Learning and Coherent Dynamics Estimation. In arXiv:1511.06654, 2015. | |||||||||||
JointMC 19. | 26.5 | 35.6 ±18.9 | 45.1 | 23.2% | 39.3% | 10,580 | 28,508 | 457 (8.5) | 969 (18.1) | 0.6 | Public |
M. Keuper, S. Tang, B. Andres, T. Brox, B. Schiele. Motion Segmentation amp; Multiple Object Tracking by Correlation Co-Clustering. In IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018. | |||||||||||
MCF_PHD 20. | 33.0 | 29.9 ±20.0 | 38.2 | 11.9% | 44.0% | 8,892 | 33,529 | 656 (14.4) | 989 (21.8) | 12.2 | Public |
N. Wojke, D. Paulus. Global data association for the Probability Hypothesis Density filter using network flows. In 2016 IEEE International Conference on Robotics and Automation, ICRA, 2016. | |||||||||||
Tracker | Avg Rank | MOTA | IDF1 | MT | ML | FP | FN | ID Sw. | Frag | Hz | Detector |
GSCR 21. | 48.7 | 15.8 ±10.5 | 27.9 | 1.8% | 61.0% | 7,597 | 43,633 | 514 (17.7) | 1,010 (34.8) | 28.1 | Public |
L. Fagot-Bouquet, R. Audigier, Y. Dhome, F. Lerasle. Online multi-person tracking based on global sparse collaborative representations. In ICIP, 2015. | |||||||||||
CEM 22. | 48.3 | 19.3 ±17.5 | 0.0 | 8.5% | 46.5% | 14,180 | 34,591 | 813 (18.6) | 1,023 (23.4) | 1.1 | Public |
A. Milan, S. Roth, K. Schindler. Continuous Energy Minimization for Multitarget Tracking. In IEEE TPAMI, 2014. | |||||||||||
DeepMP 23. | 21.5 | 40.5 ±12.8 | 28.8 | 16.8% | 35.2% | 6,279 | 29,654 | 599 (11.6) | 1,034 (20.0) | 9.6 | Public |
Anonymous submission | |||||||||||
HAM_INTP15 24. | 30.5 | 28.6 ±13.8 | 41.4 | 10.0% | 44.0% | 7,485 | 35,910 | 460 (11.1) | 1,038 (25.0) | 18.7 | Public |
Y. Yoon, A. Boragule, Y. Song, K. Yoon, M. Jeon. Online Multi-Object Tracking with Historical Appearance Matching and Scene Adaptive Detection Filtering. In IEEE AVSS, 2018. | |||||||||||
MotiCon 25. | 54.5 | 23.1 ±16.4 | 29.4 | 4.7% | 52.0% | 10,404 | 35,844 | 1,018 (24.4) | 1,061 (25.5) | 1.4 | Public |
L. Leal-Taixé, M. Fenzi, A. Kuznetsova, B. Rosenhahn, S. Savarese. Learning an image-based motion context for multiple people tracking. In CVPR, 2014. | |||||||||||
MPNTrack15 26. | 18.8 | 48.3 ±12.0 | 56.5 | 32.2% | 24.3% | 9,640 | 21,629 | 504 (7.8) | 1,074 (16.6) | 9.3 | Public |
Anonymous submission | |||||||||||
siam 27. | 38.2 | 33.0 ±17.0 | 36.2 | 8.9% | 43.3% | 5,101 | 35,190 | 853 (20.0) | 1,078 (25.2) | 1.9 | Public |
Anonymous submission | |||||||||||
LFNF 28. | 35.8 | 31.6 ±12.3 | 33.1 | 9.6% | 41.7% | 5,943 | 35,095 | 961 (22.4) | 1,106 (25.8) | 4.0 | Public |
Sheng H, Hao L, Chen J, et al. Robust Local Effective Matching Model for Multi-Target Tracking. In PCM, 2017 | |||||||||||
MHTREID15 29. | 26.3 | 40.0 ±16.2 | 49.4 | 29.7% | 24.4% | 12,780 | 23,378 | 684 (11.0) | 1,112 (17.9) | 0.5 | Public |
Anonymous submission | |||||||||||
MR 30. | 29.8 | 36.6 ±16.6 | 47.2 | 33.1% | 21.5% | 16,696 | 21,428 | 850 (13.1) | 1,156 (17.8) | 0.3 | Public |
Anonymous submission | |||||||||||
Tracker | Avg Rank | MOTA | IDF1 | MT | ML | FP | FN | ID Sw. | Frag | Hz | Detector |
DAC_min 31. | 32.6 | 28.3 ±13.4 | 38.3 | 9.8% | 45.5% | 8,396 | 35,122 | 543 (12.7) | 1,162 (27.1) | 11.6 | Public |
mLK 32. | 25.5 | 35.1 ±12.9 | 47.5 | 12.3% | 38.3% | 5,678 | 33,815 | 383 (8.5) | 1,175 (26.1) | 1.0 | Public |
Yuan Zhang, Di Xie and Shiliang Pu (Hikvision Research Institute) | |||||||||||
RSCNN 33. | 37.8 | 29.5 ±23.9 | 37.0 | 12.9% | 36.3% | 11,866 | 30,474 | 976 (19.4) | 1,176 (23.3) | 4.0 | Public |
Heba Mahgoub, Khaled Mostafa, Khaled T. Wassif and Ibrahim Farag, “Multi-Target Tracking Using Hierarchical Convolutional Features and Motion Cues” International Journal of Advanced Computer Science and Applications(IJACSA), 8(11), 2017. | |||||||||||
SCEA 34. | 38.8 | 29.1 ±12.2 | 37.2 | 8.9% | 47.3% | 6,060 | 36,912 | 604 (15.1) | 1,182 (29.6) | 6.8 | Public |
J. Yoon, C. Lee, M. Yang, K. Yoon. Online Multi-object Tracking via Structural Constraint Event Aggregation. In IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), 2016. | |||||||||||
Goturn15 35. | 52.8 | 23.9 ±14.6 | 22.3 | 3.6% | 66.4% | 7,021 | 38,750 | 965 (26.1) | 1,237 (33.5) | 3.9 | Public |
Anonymous submission | |||||||||||
SAS_MOT15 36. | 52.8 | 22.2 ±13.8 | 27.2 | 3.1% | 61.6% | 5,591 | 41,531 | 700 (21.6) | 1,240 (38.3) | 8.9 | Public |
A. Maksai, P. Fua. Eliminating Exposure Bias and Metric Mismatch in Multiple Object Tracking. In CVPR, 2019. | |||||||||||
OMT_DFH 37. | 42.3 | 21.2 ±17.2 | 37.3 | 7.1% | 46.5% | 13,218 | 34,657 | 563 (12.9) | 1,255 (28.8) | 28.6 | Public |
J. Ju, D. Kim, B. Ku, D. Han, H. Ko. Online multi-object tracking with efficient track drift and fragmentation handling. In J. Opt. Soc. Am. A, 2017. | |||||||||||
AP_HWDPL_p 38. | 21.1 | 38.5 ±9.9 | 47.1 | 8.7% | 37.4% | 4,005 | 33,203 | 586 (12.8) | 1,263 (27.5) | 6.7 | Public |
C. Long, A. Haizhou, S. Chong, Z. Zijie, B. Bo. Online Multi-Object Tracking with Convolutional Neural Networks. In 2017 IEEE International Conference on Image Processing (ICIP), 2017. | |||||||||||
GMPHD 39. | 48.3 | 18.5 ±12.7 | 28.4 | 3.9% | 55.3% | 7,864 | 41,766 | 459 (14.3) | 1,266 (39.5) | 19.8 | Public |
Y. Song, M. Jeon. Online Multiple Object Tracking with the Hierarchically Adopted GM-PHD Filter using Motion and Appearance. In IEEE/IEIE The International Conference on Consumer Electronics (ICCE) Asia, 2016. | |||||||||||
HybridDAT 40. | 26.0 | 35.0 ±15.0 | 47.7 | 11.4% | 42.2% | 8,455 | 31,140 | 358 (7.3) | 1,267 (25.7) | 4.6 | Public |
M. Yang, Y. Jia. A Hybrid Data Association Framework for Robust Online Multi-Object Tracking. In IEEE Transactions on Image Processing, 2016. | |||||||||||
Tracker | Avg Rank | MOTA | IDF1 | MT | ML | FP | FN | ID Sw. | Frag | Hz | Detector |
CRF_RNN15 41. | 22.2 | 38.9 ±15.1 | 49.3 | 20.9% | 29.4% | 10,669 | 26,291 | 591 (10.3) | 1,270 (22.2) | 3.2 | Public |
Anonymous submission | |||||||||||
dSRPN15 42. | 36.3 | 33.3 ±15.3 | 32.7 | 9.3% | 43.7% | 7,825 | 32,211 | 919 (19.3) | 1,276 (26.8) | 3.9 | Public |
Anonymous submission | |||||||||||
RMOT 43. | 54.0 | 18.6 ±17.5 | 32.6 | 5.3% | 53.3% | 12,473 | 36,835 | 684 (17.1) | 1,282 (32.0) | 7.9 | Public |
J. Yoon, H. Yang, J. Lim, K. Yoon. Bayesian Multi-Object Tracking Using Motion Context from Multiple Objects. In IEEE Winter Conference on Applications of Computer Vision (WACV), 2015. | |||||||||||
AdTobKF 44. | 38.3 | 24.8 ±12.1 | 34.5 | 4.0% | 52.0% | 6,201 | 39,321 | 666 (18.5) | 1,300 (36.1) | 206.5 | Public |
K. Loumponias, A. Dimou, N. Vretos, P. Daras. Adaptive Tobit Kalman-Based Tracking. In 2018 14th International Conference on Signal-Image Technology \& Internet-Based Systems (SITIS), 2018. | |||||||||||
TENSOR 45. | 51.6 | 24.3 ±13.2 | 24.1 | 5.5% | 46.6% | 6,644 | 38,582 | 1,271 (34.2) | 1,304 (35.1) | 24.0 | Public |
X. Shi, H. Ling, Y. Pang, W. Hu, P. Chu, J. Xing. Rank-1 Tensor Approximation for High-Order Association in Multi-target Tracking. In IJCV, 2019. | |||||||||||
SiameseCNN 46. | 38.3 | 29.0 ±15.1 | 34.3 | 8.5% | 48.4% | 5,160 | 37,798 | 639 (16.6) | 1,316 (34.2) | 52.8 | Public |
Laura Leal-Taixé, Cristian Canton-Ferrer, Konrad Schindler. Learning by Tracking: Siamese CNN for Robust Target Association. DeepVision Workshop (CVPR), Las Vegas (Nevada, USA), June 2016. | |||||||||||
GMPHD_OGM 47. | 29.4 | 30.7 ±12.6 | 38.8 | 11.5% | 38.1% | 6,502 | 35,030 | 1,034 (24.1) | 1,351 (31.4) | 169.5 | Public |
Y. Song, K. Yoon, Y. Yoon, K. Yow, M. Jeon. Online Multi-Object Tracking with GMPHD Filter and Occlusion Group Management. In IEEE Access, 2019. | |||||||||||
SRPN 48. | 43.3 | 31.0 ±13.3 | 30.7 | 12.6% | 41.7% | 10,241 | 31,099 | 1,062 (21.5) | 1,370 (27.7) | 3.9 | Public |
Anonymous submission | |||||||||||
TLO 49. | 30.3 | 41.3 ±13.7 | 46.1 | 15.7% | 34.5% | 8,000 | 27,210 | 852 (15.3) | 1,405 (25.2) | 5.0 | Public |
Anonymous submission | |||||||||||
QuadMOT 50. | 32.9 | 33.8 ±14.8 | 40.4 | 12.9% | 36.9% | 7,898 | 32,061 | 703 (14.7) | 1,430 (29.9) | 3.7 | Public |
J. Son, M. Baek, M. Cho, B. Han. Multi-Object Tracking with Quadruplet Convolutional Neural Networks. In CVPR, 2017. | |||||||||||
Tracker | Avg Rank | MOTA | IDF1 | MT | ML | FP | FN | ID Sw. | Frag | Hz | Detector |
KCF 51. | 27.9 | 38.9 ±14.5 | 44.5 | 16.6% | 31.5% | 7,321 | 29,501 | 720 (13.9) | 1,440 (27.7) | 0.3 | Public |
P. Chu, H. Fan, C. Tan, H. Ling. Online Multi-Object Tracking with Instance-Aware Tracker and Dynamic Model Refreshment. In WACV, 2019. | |||||||||||
AM 52. | 25.4 | 34.3 ±13.7 | 48.3 | 11.4% | 43.4% | 5,154 | 34,848 | 348 (8.0) | 1,463 (33.8) | 0.5 | Public |
Q. Chu, W. Ouyang, H. Li, X. Wang, B. Liu, N. Yu. Online Multi-object Tracking Using CNN-Based Single Object Tracker with Spatial-Temporal Attention Mechanism. In 2017 IEEE International Conference on Computer Vision (ICCV), 2017. | |||||||||||
EAMTTpub 53. | 47.8 | 22.3 ±14.2 | 32.8 | 5.4% | 52.7% | 7,924 | 38,982 | 833 (22.8) | 1,485 (40.6) | 12.2 | Public |
R. Sanchez-Matilla, F. Poiesi, A. Cavallaro "Multi-target tracking with strong and weak detections" in BMTT ECCVw 2016 | |||||||||||
MDP 54. | 35.7 | 30.3 ±14.6 | 44.7 | 13.0% | 38.4% | 9,717 | 32,422 | 680 (14.4) | 1,500 (31.8) | 1.1 | Public |
Y. Xiang, A. Alahi, S. Savarese. Learning to Track: Online Multi-Object Tracking by Decision Making. In International Conference on Computer Vision (ICCV), 2015. | |||||||||||
TDAM 55. | 32.5 | 33.0 ±9.8 | 46.1 | 13.3% | 39.1% | 10,064 | 30,617 | 464 (9.2) | 1,506 (30.0) | 5.9 | Public |
M. Yang, Y. Jia. Temporal dynamic appearance modeling for online multi-person tracking. In Computer Vision and Image Understanding, 2016. | |||||||||||
TBSS15 56. | 41.1 | 29.2 ±12.5 | 37.2 | 6.8% | 43.8% | 6,068 | 36,779 | 649 (16.2) | 1,508 (37.6) | 11.5 | Public |
X. Zhou, P. Jiang, Z. Wei, H. Dong, F. Wang. Online Multi-Object Tracking with Structural Invariance Constraint. In BMVC, 2018. | |||||||||||
CRFTrack_ 57. | 23.1 | 40.0 ±14.5 | 49.6 | 23.0% | 28.6% | 10,295 | 25,917 | 658 (11.4) | 1,508 (26.1) | 3.2 | Public |
Anonymous submission | |||||||||||
RAR15pub 58. | 28.9 | 35.1 ±12.5 | 45.4 | 13.0% | 42.3% | 6,771 | 32,717 | 381 (8.1) | 1,523 (32.6) | 5.4 | Public |
K. Fang, Y. Xiang, X. Li, S. Savarese. Recurrent Autoregressive Networks for Online Multi-Object Tracking. In The IEEE Winter Conference on Applications of Computer Vision (WACV), 2018. | |||||||||||
TBX 59. | 49.6 | 27.5 ±13.3 | 33.8 | 10.4% | 45.8% | 7,968 | 35,810 | 759 (18.2) | 1,528 (36.6) | 0.1 | Public |
R. Henschel, L. Leal-Taixé, B. Rosenhahn, K. Schindler. Tracking with multi-level features. In arXiv:1607.07304, 2016. | |||||||||||
DEEPDA_MOT 60. | 48.5 | 22.5 ±17.7 | 25.9 | 6.4% | 62.0% | 7,346 | 39,092 | 1,159 (31.9) | 1,538 (42.3) | 172.8 | Public |
K. Yoon, D. Kim, Y. Yoon, M. Jeon. Data Association for Multi-Object Tracking via Deep Neural Networks. In Sensors, 2019. | |||||||||||
Tracker | Avg Rank | MOTA | IDF1 | MT | ML | FP | FN | ID Sw. | Frag | Hz | Detector |
TSDA_OAL 61. | 48.4 | 18.6 ±17.6 | 36.1 | 9.4% | 42.3% | 16,350 | 32,853 | 806 (17.3) | 1,544 (33.2) | 19.7 | Public |
H. Ko. Online multi-person tracking with two-stage data association and online appearance model learning. In IET Computer Vision, 2017. | |||||||||||
DCCRF 62. | 33.6 | 33.6 ±11.0 | 39.1 | 10.4% | 37.6% | 5,917 | 34,002 | 866 (19.4) | 1,566 (35.1) | 0.1 | Public |
H. Zhou, W. Ouyang, J. Cheng, X. Wang, H. Li. Deep Continuous Conditional Random Fields with Asymmetric Inter-object Constraints for Online Multi-object Tracking. In IEEE Transactions on Circuits and Systems for Video Technology, 2018. | |||||||||||
CDA_DDALpb 63. | 31.5 | 32.8 ±10.6 | 38.8 | 9.7% | 42.2% | 4,983 | 35,690 | 614 (14.7) | 1,583 (37.8) | 2.3 | Public |
S. Bae and K. Yoon, Confidence-Based Data Association and Discriminative Deep Appearance Learning for Robust Online Multi-Object Tracking , In IEEE TPAMI, 2017. | |||||||||||
MMHT15 64. | 38.3 | 29.8 ±17.0 | 38.0 | 12.1% | 38.0% | 10,548 | 31,390 | 1,189 (24.3) | 1,612 (33.0) | 12.1 | Public |
Anonymous submission | |||||||||||
TLO15 65. | 32.8 | 40.0 ±14.9 | 44.3 | 17.1% | 28.8% | 9,349 | 26,328 | 1,207 (21.1) | 1,624 (28.4) | 12.1 | Public |
Anonymous submission | |||||||||||
oICF 66. | 44.1 | 27.1 ±14.9 | 40.5 | 6.4% | 48.7% | 7,594 | 36,757 | 454 (11.3) | 1,660 (41.3) | 1.4 | Public |
H. Kieritz, S. Becker, W. Hübner, M. Arens. Online Multi-Person Tracking using Integral Channel Features. In IEEE Advanced Video and Signal-based Surveillance (AVSS) 2016, 2016. | |||||||||||
DCOR 67. | 47.1 | 22.4 ±12.1 | 24.7 | 3.3% | 57.4% | 5,603 | 41,410 | 634 (19.4) | 1,686 (51.7) | 37.6 | Public |
Anonymous submission | |||||||||||
CF_MCMC 68. | 36.6 | 31.4 ±11.3 | 36.4 | 10.3% | 40.9% | 8,798 | 32,541 | 814 (17.3) | 1,711 (36.4) | 3.2 | Public |
Anonymous submission | |||||||||||
LP2D 69. | 52.1 | 19.8 ±14.2 | 0.0 | 6.7% | 41.2% | 11,580 | 36,045 | 1,649 (39.9) | 1,712 (41.4) | 112.1 | Public |
MOT baseline: Linear programming on 2D image coordinates. | |||||||||||
TC_ODAL 70. | 65.4 | 15.1 ±15.0 | 0.0 | 3.2% | 55.8% | 12,970 | 38,538 | 637 (17.1) | 1,716 (46.0) | 1.7 | Public |
S. Bae, K. Yoon. Robust Online Multi-Object Tracking based on Tracklet Confidence and Online Discriminative Appearance Learning. In CVPR, 2014. | |||||||||||
Tracker | Avg Rank | MOTA | IDF1 | MT | ML | FP | FN | ID Sw. | Frag | Hz | Detector |
PoMOT 71. | 63.1 | 16.7 ±13.8 | 28.8 | 5.0% | 50.3% | 10,185 | 40,025 | 968 (27.8) | 1,748 (50.2) | 0.3 | Public |
Anonymous submission | |||||||||||
RKCF 72. | 59.7 | 16.8 ±13.5 | 29.0 | 5.5% | 50.1% | 10,336 | 39,805 | 980 (27.8) | 1,750 (49.7) | 6.2 | Public |
Anonymous submission | |||||||||||
Tracktor15 73. | 32.7 | 44.1 ±11.7 | 46.7 | 18.0% | 26.2% | 6,477 | 26,577 | 1,318 (23.2) | 1,790 (31.5) | 0.9 | Public |
P. Bergmann, T. Meinhardt, L. Leal-Taixé. Tracking without bells and whistles. In ICCV, 2019. | |||||||||||
DPT 74. | 61.1 | 16.1 ±12.1 | 27.5 | 5.0% | 50.3% | 10,330 | 40,154 | 1,076 (31.1) | 1,794 (51.8) | 0.4 | Public |
ELP 75. | 48.6 | 25.0 ±10.8 | 26.2 | 7.5% | 43.8% | 7,345 | 37,344 | 1,396 (35.6) | 1,804 (46.0) | 5.7 | Public |
N. McLaughlin, J. Martinez Del Rincon, P. Miller. Enhancing Linear Programming with Motion Modeling for Multi-target Tracking. In IEEE Winter Conference on Applications of Computer Vision (WACV), 2015. | |||||||||||
GMMA_intp 76. | 40.3 | 27.3 ±12.0 | 36.6 | 6.5% | 43.1% | 7,848 | 35,817 | 987 (23.7) | 1,848 (44.3) | 132.5 | Public |
Y. Song, Y. Yoon, K. Yoon, M. Jeon. Online and Real-Time Tracking with the GMPHD Filter using Group Management and Relative Motion Analysis. In Proc. IEEE Int. Workshop Traffic Street Surveill. Safety Secur. (AVSS), 2018. | |||||||||||
EDA_GNN 77. | 46.3 | 21.8 ±13.8 | 27.8 | 9.0% | 40.2% | 11,970 | 34,587 | 1,488 (34.0) | 1,851 (42.4) | 56.4 | Public |
Paper ID 2713 | |||||||||||
ALExTRAC 78. | 60.1 | 17.0 ±12.1 | 17.3 | 3.9% | 52.4% | 9,233 | 39,933 | 1,859 (53.1) | 1,872 (53.5) | 3.7 | Public |
A. Bewley, L. Ott, F. Ramos, B. Upcroft. ALExTRAC: Affinity Learning by Exploring Temporal Reinforcement within Association Chains. In International Conference on Robotics and Automation (ICRA), (to appear) 2016. | |||||||||||
FP_H 79. ![]() | 45.5 | 23.4 ±12.8 | 33.7 | 3.7% | 55.9% | 5,782 | 40,719 | 538 (16.0) | 1,875 (55.6) | 33.4 | Public |
Anonymous submission | |||||||||||
TBD 80. | 65.7 | 15.9 ±17.6 | 0.0 | 6.4% | 47.9% | 14,943 | 34,777 | 1,939 (44.7) | 1,963 (45.2) | 0.7 | Public |
A. Geiger, M. Lauer, C. Wojek, C. Stiller, R. Urtasun. 3D Traffic Scene Understanding from Movable Platforms. In Pattern Analysis and Machine Intelligence (PAMI), 2014. | |||||||||||
Tracker | Avg Rank | MOTA | IDF1 | MT | ML | FP | FN | ID Sw. | Frag | Hz | Detector |
CppSORT 81. | 50.5 | 21.7 ±11.8 | 26.8 | 3.7% | 49.1% | 8,422 | 38,454 | 1,231 (32.9) | 2,005 (53.6) | 1,112.1 | Public |
S. Murray. Real-Time Multiple Object Tracking - A Study on the Importance of Speed. In arXiv preprint arXiv:1709.03572, 2017. | |||||||||||
AMIR15 82. | 28.4 | 37.6 ±12.5 | 46.0 | 15.8% | 26.8% | 7,933 | 29,397 | 1,026 (19.7) | 2,024 (38.8) | 1.9 | Public |
A. Sadeghian, A. Alahi, S. Savarese. Tracking The Untrackable: Learning To Track Multiple Cues with Long-Term Dependencies. In ICCV, 2017. | |||||||||||
BiGRU1 83. | 44.3 | 26.1 ±16.5 | 32.2 | 6.5% | 48.8% | 5,761 | 38,948 | 719 (19.6) | 2,046 (55.9) | 4.0 | Public |
Anonymous submission | |||||||||||
RNN_LSTM 84. | 57.3 | 19.0 ±15.2 | 17.1 | 5.5% | 45.6% | 11,578 | 36,706 | 1,490 (37.0) | 2,081 (51.7) | 165.2 | Public |
A. Milan, S. Rezatofighi, A. Dick, I. Reid, K. Schindler. Online Multi-Target Tracking using Recurrent Neural Networks. In AAAI, 2017. | |||||||||||
SMOTe 85. | 41.8 | 28.0 ±16.1 | 45.4 | 15.0% | 30.8% | 15,881 | 27,372 | 977 (17.6) | 2,106 (38.0) | 1.6 | Public |
Anonymous submission | |||||||||||
SMOT 86. | 67.5 | 18.2 ±10.3 | 0.0 | 2.8% | 54.8% | 8,780 | 40,310 | 1,148 (33.4) | 2,132 (62.0) | 2.7 | Public |
C. Dicle, O. Camps, M. Sznaier. The Way They Move: Tracking Targets with Similar Appearance. In ICCV, 2013. | |||||||||||
PHD_GSDL 87. | 41.2 | 30.5 ±14.9 | 38.8 | 7.6% | 41.2% | 6,534 | 35,284 | 879 (20.6) | 2,208 (51.9) | 8.2 | Public |
Z. Fu, P. Feng, F. Angelini, J. Chambers, S. Naqvi. Particle PHD Filter based Multiple Human Tracking using Online Group-Structured Dictionary Learning. In IEEE Access, 2018. | |||||||||||
MTSTracker 88. | 49.3 | 20.6 ±18.2 | 31.9 | 9.0% | 36.9% | 15,161 | 32,212 | 1,387 (29.2) | 2,357 (49.5) | 19.3 | Public |
F. Nguyen Thi Lan Anh, F. Bremond. Multi-Object Tracking using Multi-Channel Part Appearance Representation. In International conference on Advanced video and Signal Based Surveillance, 2017. | |||||||||||
SNM 89. | 38.6 | 31.3 ±16.5 | 38.2 | 12.6% | 35.4% | 8,903 | 32,393 | 926 (19.6) | 2,382 (50.4) | 14.8 | Public |
Anonymous submission | |||||||||||
KCF_Simple 90. | 59.1 | 18.3 ±11.1 | 25.1 | 2.6% | 49.8% | 8,976 | 39,805 | 1,436 (40.8) | 2,634 (74.8) | 35.6 | Public |
Anonymous submission | |||||||||||
Tracker | Avg Rank | MOTA | IDF1 | MT | ML | FP | FN | ID Sw. | Frag | Hz | Detector |
STRN 91. | 31.3 | 38.1 ±11.3 | 46.6 | 11.5% | 33.4% | 5,451 | 31,571 | 1,033 (21.2) | 2,665 (54.8) | 13.8 | Public |
J. Xu, Y. Cao, Z. Zhang, H. Hu. Spatial-Temporal Relation Networks for Multi-Object Tracking. In ICCV, 2019. | |||||||||||
HSJ_Sia 92. | 53.8 | 20.9 ±13.0 | 29.2 | 4.0% | 51.6% | 6,457 | 40,477 | 1,695 (49.7) | 2,734 (80.1) | 70.3 | Public |
Anonymous submission | |||||||||||
INARLA 93. | 36.4 | 34.7 ±13.2 | 42.1 | 12.5% | 30.0% | 9,855 | 29,158 | 1,112 (21.2) | 2,848 (54.2) | 2.6 | Public |
H. Wu, Y. Hu, K. Wang, H. Li, L. Nie, H. Cheng. Instance-aware representation learning and association for online multi-person tracking. In Pattern Recognition, 2019. | |||||||||||
LDCT 94. | 50.6 | 4.7 ±41.3 | 16.8 | 11.4% | 32.5% | 14,066 | 32,156 | 12,348 (259.1) | 2,918 (61.2) | 20.7 | Public |
F. Solera, S. Calderara, R. Cucchiara. Learning to Divide and Conquer for Online Multi-Target Tracking. In ICCV, 2015 | |||||||||||
DP_NMS 95. | 52.2 | 14.5 ±14.5 | 19.7 | 6.0% | 40.8% | 13,171 | 34,814 | 4,537 (104.7) | 3,090 (71.3) | 444.8 | Public |
H. Pirsiavash, D. Ramanan, C. Fowlkes. Globally-Optimal Greedy Algorithms for Tracking a Variable Number of Objects. In CVPR, 2011. | |||||||||||
HOHOTRACK 96. | 37.9 | 26.8 ±21.7 | 32.9 | 28.6% | 16.9% | 18,994 | 24,549 | 1,411 (23.5) | 3,417 (56.9) | 26.7 | Public |
Anonymous submission | |||||||||||
SORT_Y 97. | 52.1 | 11.8 ±18.5 | 26.1 | 10.0% | 33.4% | 19,803 | 31,476 | 2,893 (59.3) | 3,801 (77.9) | 334.8 | Public |
Anonymous submission |
Sequences | Frames | Trajectories | Boxes |
11 | 5783 | 721 | 61440 |
Sequence difficulty (from easiest to hardest, measured by average MOTA)
...
...
Measure | Better | Perfect | Description |
Avg Rank | lower | 1 | This is the rank of each tracker averaged over all present evaluation measures. |
MOTA | higher | 100 % | Multiple Object Tracking Accuracy [1]. This measure combines three error sources: false positives, missed targets and identity switches. |
MOTP | higher | 100 % | Multiple Object Tracking Precision [1]. The misalignment between the annotated and the predicted bounding boxes. |
IDF1 | higher | 100 % | ID F1 Score [2]. The ratio of correctly identified detections over the average number of ground-truth and computed detections. |
FAF | lower | 0 | The average number of false alarms per frame. |
MT | higher | 100 % | Mostly tracked targets. The ratio of ground-truth trajectories that are covered by a track hypothesis for at least 80% of their respective life span. |
ML | lower | 0 % | Mostly lost targets. The ratio of ground-truth trajectories that are covered by a track hypothesis for at most 20% of their respective life span. |
FP | lower | 0 | The total number of false positives. |
FN | lower | 0 | The total number of false negatives (missed targets). |
ID Sw. | lower | 0 | The total number of identity switches. Please note that we follow the stricter definition of identity switches as described in [3]. |
Frag | lower | 0 | The total number of times a trajectory is fragmented (i.e. interrupted during tracking). |
Hz | higher | Inf. | Processing speed (in frames per second excluding the detector) on the benchmark. |
Symbol | Description |
This is an online (causal) method, i.e. the solution is immediately available with each incoming frame and cannot be changed at any later time. | |
This method used the provided detection set as input. | |
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This entry has been submitted or updated less than a week ago. |
[1] | Evaluating Multiple Object Tracking Performance: The CLEAR MOT Metrics. Image and Video Processing, 2008(1):1-10, 2008. |
[2] | Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking. In ECCV workshop on Benchmarking Multi-Target Tracking, 2016. |
[3] | Learning to associate: HybridBoosted multi-target tracker for crowded scene. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2009. |