MOT17 Results

Download all results for this benchmark

Click on a measure to sort the table accordingly. See below for a more detailed description.


Showing only entries that use public detections!

Benchmark Statistics

TrackerMOTAIDF1HOTAMTMLFPFNRcllPrcnAssADetAAssReAssPrDetReDetPrLocAFAFID Sw.FragHz
SCOSORT
1. online method using public detections 		81.0 79.8 64.8 1,314 (55.8)348 (14.8)23,406 82,509 85.4 95.4 64.7 65.3 70.5 78.8 70.5 78.8 83.2 1.3 1,347 (0.0)1,956 (0.0)69.3
ling
2. using public detections 		80.5 78.6 64.2 1,297 (55.1)345 (14.6)26,022 82,492 85.4 94.9 63.7 65.1 69.2 79.1 70.6 78.5 83.2 1.5 1,566 (0.0)1,829 (0.0)39.7
wyh1
3. using public detections 		80.3 78.4 64.2 1,284 (54.5)324 (13.8)30,725 79,209 86.0 94.0 63.8 65.1 69.2 79.2 71.1 77.8 83.1 1.7 1,296 (0.0)1,640 (0.0)21.5
FeatureSORT
4. online method using public detections 		79.6 77.2 63.0 1,264 (53.7)348 (14.8)29,588 83,132 85.3 94.2 62.0 64.4 68.2 76.0 70.4 77.7 83.1 1.7 2,269 (0.0)2,148 (0.0)8.7
H. Hashempoor, R. Koikara, Y. Hwang. FeatureSORT: Essential Features for Effective Tracking. In , 2024.
OAIE
5. online method using public detections 		78.9 78.5 63.2 1,178 (50.0)371 (15.8)21,979 94,439 83.3 95.5 62.9 63.7 69.1 76.1 68.7 78.8 83.2 1.2 2,422 (0.0)3,712 (0.0)25.4
PermaTrack
6. online method using public detections 		73.1 67.2 54.2 996 (42.3)450 (19.1)24,577 123,508 78.1 94.7 51.2 58.0 59.6 67.9 62.8 76.1 81.6 1.4 3,571 (0.0)5,826 (0.0)11.9
P. Tokmakov, J. Li, W. Burgard, A. Gaidon. Learning to Track with Object Permanence. In ICCV, 2021.
MOTer
7. online method using public detections 		71.9 62.3 54.1 894 (38.0)534 (22.7)17,378 137,008 75.7 96.1 49.9 59.0 54.3 76.0 63.0 80.0 83.7 1.0 4,046 (0.0)7,929 (0.0)1.0
Y. Xu, Y. Ban, G. Delorme, C. Gan, D. Rus, X. Alameda-Pineda. TransCenter: Transformers with Dense Queries for Multiple-Object Tracking. In arXiv, 2021.
PixelGuide
8. online method using public detections 		69.7 68.4 55.5 903 (38.3)615 (26.1)26,871 140,457 75.1 94.0 55.1 56.3 60.7 74.4 60.9 76.3 81.6 1.5 3,639 (0.0)4,155 (0.0)8.9
A. Boragule, H. Jang, N. Ha, M. Jeon. Pixel-Guided Association for Multi-Object Tracking. In Sensors, 2022.
NCT
9. using public detections 		69.5 68.5 54.6 1,092 (46.4)399 (16.9)65,463 101,471 82.0 87.6 53.7 56.1 59.6 71.5 64.7 69.1 79.9 3.7 4,919 (0.0)4,054 (0.0)13.9
K. Zeng, Y. You, T. Shen, Q. Wang, Z. Tao, Z. Wang, Q. Liu. NCT:noise-control multi-object tracking. In Complex & Intelligent Systems, 2023.
OUTrack_fm_p
10. online method using public detections 		69.0 66.8 54.8 885 (37.6)464 (19.7)28,795 141,580 74.9 93.6 52.9 57.1 59.2 73.2 62.0 77.4 83.0 1.6 4,472 (0.0)8,723 (0.0)27.6
Q. Liu, D. Chen, Q. Chu, L. Yuan, B. Liu, L. Zhang, N. Yu. Online Multi-Object Tracking with Unsupervised Re-Identification Learning and Occlusion Estimation. In Neurocomputing, 2022.
TrackerMOTAIDF1HOTAMTMLFPFNRcllPrcnAssADetAAssReAssPrDetReDetPrLocAFAFID Sw.FragHz
TransCtr
11. online method using public detections 		68.8 61.4 51.4 867 (36.8)564 (23.9)22,860 149,188 73.6 94.8 47.7 56.0 52.8 73.1 60.2 77.6 82.4 1.3 4,102 (0.0)8,468 (0.0)1.0
Y. Xu, Y. Ban, G. Delorme, C. Gan, D. Rus, X. Alameda-Pineda. TransCenter: Transformers with Dense Queries for Multiple-Object Tracking. In arXiv, 2021.
TrajEocc
12. using public detections 		67.8 61.4 50.0 848 (36.0)577 (24.5)20,950 157,460 72.1 95.1 46.9 53.9 55.4 67.3 57.9 76.4 81.4 1.2 3,475 (0.0)5,663 (0.0)1.4
A. Girbau, X. Gir'o-i-Nieto, I. Rius, F. Marqu'es. Multiple Object Tracking with Mixture Density Networks for Trajectory Estimation. In arXiv preprint arXiv:2106.10950, 2021.
BYTE_Pub
13. online method using public detections 		67.4 70.0 56.1 730 (31.0)735 (31.2)9,939 172,636 69.4 97.5 57.5 54.9 64.8 73.9 57.7 81.1 83.8 0.6 1,331 (0.0)2,387 (0.0)27.4
Y. Zhang, P. Sun, Y. Jiang, D. Yu, F. Weng, Z. Yuan, P. Luo, W. Liu, X. Wang. ByteTrack: Multi-Object Tracking by Associating Every Detection Box. In Proceedings of the European Conference on Computer Vision (ECCV), 2022.
TrajE
14. online method using public detections 		67.4 61.2 49.7 820 (34.8)587 (24.9)18,652 161,347 71.4 95.6 46.6 53.5 54.4 67.8 57.3 76.7 81.5 1.1 4,019 (0.0)6,613 (0.0)1.4
A. Girbau, X. Gir'o-i-Nieto, I. Rius, F. Marqu'es. Multiple Object Tracking with Mixture Density Networks for Trajectory Estimation. In arXiv preprint arXiv:2106.10950, 2021.
MAT
15. online method using public detections 		67.1 69.2 56.0 917 (38.9)622 (26.4)22,756 161,547 71.4 94.7 57.2 55.1 62.8 76.6 59.2 78.5 83.1 1.3 1,279 (17.9)2,037 (28.5)11.5
MAT: Motion-Aware Multi-Object Tracking
HTracker
16. using public detections 		66.9 70.4 55.3 667 (28.3)490 (20.8)30,704 151,001 73.2 93.1 55.5 55.3 60.9 74.8 59.9 76.2 81.6 1.7 4,806 (65.6)10,994 (150.1)36.7
Xudong Zhang. Boosting the tracking speed for real-time application.
hugmot
17. online method using public detections 		64.8 62.8 49.4 738 (31.3)645 (27.4)16,174 180,371 68.0 96.0 48.4 50.8 54.1 71.0 54.0 76.2 80.7 0.9 2,102 (30.9)7,750 (113.9)92.5
Multiple Object Tracking by Tracjectory Map Regression with Temporal Priors Embedding
UTM
18. online method using public detections 		63.5 65.1 52.5 881 (37.4)635 (27.0)33,683 170,352 69.8 92.1 53.2 52.2 59.7 73.4 57.0 75.2 81.9 1.9 1,686 (0.0)2,562 (0.0)13.1
S. You, H. Yao, k. Bao, C. Xu. UTM: A Unified Multiple Object Tracking Model with Identity-Aware Feature Enhancement. In CVPR, 2023.
MPTC
19. online method using public detections 		62.6 65.8 51.7 627 (26.6)750 (31.8)8,824 198,338 64.8 97.6 53.1 50.6 57.6 75.5 53.1 79.9 82.5 0.5 4,074 (0.0)5,534 (0.0)1.2
D. Stadler, J. Beyerer. Multi-Pedestrian Tracking with Clusters. In 2021 17th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), 2021.
TMOH
20. online method using public detections 		62.1 62.8 50.4 633 (26.9)739 (31.4)10,951 201,195 64.3 97.1 50.9 50.2 54.8 77.2 52.7 79.5 82.4 0.6 1,897 (29.5)4,622 (71.8)0.7
D. Stadler, J. Beyerer. Improving Multiple Pedestrian Tracking by Track Management and Occlusion Handling. In CVPR, 2021.
TrackerMOTAIDF1HOTAMTMLFPFNRcllPrcnAssADetAAssReAssPrDetReDetPrLocAFAFID Sw.FragHz
SUSHI
21. using public detections 		62.0 71.5 54.6 801 (34.0)741 (31.5)29,428 183,825 67.4 92.8 59.5 50.3 65.3 76.5 54.3 74.8 80.8 1.7 1,041 (0.0)2,332 (0.0)21.1
O. Cetintas, G. Brasó, L. Leal-Taixé. Unifying Short and Long-Term Tracking with Graph Hierarchies. In The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2023.
UnsupTrack
22. online method using public detections 		61.7 58.1 46.9 640 (27.2)762 (32.4)16,872 197,632 65.0 95.6 45.2 48.9 53.8 64.5 52.0 76.5 81.2 1.0 1,864 (28.7)4,213 (64.8)2.0
S. Karthik, A. Prabhu, V. Gandhi. Simple Unsupervised Multi-Object Tracking. In Arxiv, 2020.
Sp_Con
23. using public detections 		61.5 63.3 50.5 622 (26.4)754 (32.0)14,056 200,655 64.4 96.3 52.0 49.2 55.3 79.3 52.0 77.8 81.7 0.8 2,478 (0.0)5,079 (0.0)7.7
G. Wang, Y. Wang, R. Gu, W. Hu, J. Hwang. Split and Connect: A Universal Tracklet Booster for Multi-Object Tracking. In IEEE Transactions on Multimedia, 2022.
CTTrackPub
24. online method using public detections 		61.5 59.6 48.2 621 (26.4)752 (31.9)14,076 200,672 64.4 96.3 47.8 49.0 53.9 70.6 51.9 77.6 81.7 0.8 2,583 (40.1)4,965 (77.1)17.0
X. Zhou, V. Koltun, P. Kr"ahenb"uhl. Tracking Objects as Points. In ECCV, 2020.
Lif_T
25. using public detections 		60.5 65.6 51.3 637 (27.0)791 (33.6)14,966 206,619 63.4 96.0 54.7 48.3 59.0 78.6 51.1 77.4 81.3 0.8 1,189 (18.8)3,476 (54.8)0.5
A. Hornakova, R. Henschel, B. Rosenhahn, P. Swoboda. Lifted Disjoint Paths with Application in Multiple Object Tracking. In ICML, 2020.
ApLift
26. using public detections 		60.5 65.6 51.1 798 (33.9)728 (30.9)30,609 190,670 66.2 92.4 53.5 49.1 59.6 73.4 53.2 74.2 80.7 1.7 1,709 (0.0)2,672 (0.0)1.8
A. Hornakova*, T. Kaiser*, M. Rolinek, B. Rosenhahn, P. Swoboda, R. Henschel. Making Higher Order MOT Scalable: An Efficient Approximate Solver for Lifted Disjoint Paths. In International Conference on Computer Vision (ICCV), 2021.
mfi_tst
27. online method using public detections 		60.1 58.8 47.2 612 (26.0)699 (29.7)13,503 209,475 62.9 96.3 46.4 48.3 49.7 76.7 50.9 78.0 81.5 0.8 2,065 (0.0)3,829 (0.0)2.2
J. Y, H. Ge, J. Yang, Y. Tong, S. Su. Online multi-object tracking using multi-function integration and tracking simulation training. In Applied Intelligence, 2021.
ISE_MOT17R
28. online method using public detections 		60.1 56.4 45.8 672 (28.5)661 (28.1)23,168 199,483 64.6 94.0 43.2 49.0 47.7 72.0 52.3 76.1 81.0 1.3 2,556 (39.5)3,182 (49.2)7.2
MIFT
SLA_public
29. online method using public detections 		59.7 63.4 49.1 566 (24.0)732 (31.1)16,644 209,318 62.9 95.5 50.7 47.9 55.9 74.6 50.5 76.7 80.6 0.9 1,647 (26.2)3,819 (60.7)12.8
Spatial-Attention Location-Aware Multi-Object Tracking. In , 2020.
LPC_MOT
30. using public detections 		59.0 66.8 51.5 703 (29.9)798 (33.9)23,102 206,948 63.3 93.9 56.0 47.7 62.7 74.0 51.0 75.7 80.9 1.3 1,122 (17.7)1,943 (30.7)4.8
P. Dai, R. Weng, W. Choi, C. Zhang, Z. He, W. Ding. Learning a Proposal Classifier for Multiple Object tracking. In CVPR (Accepted), 2021.
TrackerMOTAIDF1HOTAMTMLFPFNRcllPrcnAssADetAAssReAssPrDetReDetPrLocAFAFID Sw.FragHz
MPNTrack
31. using public detections 		58.8 61.7 49.0 679 (28.8)788 (33.5)17,413 213,594 62.1 95.3 51.1 47.3 57.1 74.3 50.2 76.9 81.5 1.0 1,185 (19.1)2,265 (36.4)6.5
G. Braso, L. Leal-Taixe. Learning a Neural Solver for Multiple Object Tracking. In CVPR, 2020.
STMA
32. online method using public detections 		58.8 61.6 48.4 617 (26.2)763 (32.4)17,564 213,561 62.1 95.2 49.8 47.3 56.0 72.6 50.1 76.8 81.2 1.0 1,446 (23.3)3,430 (55.2)1.2
WHY, @BUAA
IQHAT
33. using public detections 		58.4 61.8 49.0 568 (24.1)829 (35.2)15,013 218,274 61.3 95.8 51.4 46.9 56.6 75.1 49.5 77.4 81.5 0.8 1,261 (0.0)2,251 (0.0)8.1
Y. He, X. Wei, X. Hong, W. Ke, Y. Gong. Identity-Quantity Harmonic Multi-Object Tracking. In IEEE Transactions on Image Processing, 2022.
OCSORTpublic
34. online method using public detections 		58.2 65.1 52.4 432 (18.3)1,055 (44.8)4,379 230,449 59.2 98.7 57.6 47.8 63.5 76.4 49.5 82.6 84.3 0.2 784 (0.0)2,006 (0.0)28.6
J. Cao, X. Weng, R. Khirodkar, J. Pang, K. Kitani. Observation-Centric SORT: Rethinking SORT for Robust Multi-Object Tracking. In , 2022.
Lif_TsimInt
35. using public detections 		58.2 65.2 50.7 674 (28.6)791 (33.6)16,850 217,944 61.4 95.4 54.9 47.1 59.6 78.5 49.8 77.4 81.5 0.9 1,022 (16.7)2,062 (33.6)4.6
A. Hornakova, R. Henschel, B. Rosenhahn, P. Swoboda. Lifted Disjoint Paths with Application in Multiple Object Tracking. In ICML, 2020.
GNNMatch
36. online method using public detections 		57.3 56.3 45.4 575 (24.4)787 (33.4)14,100 225,042 60.1 96.0 45.2 45.9 52.5 67.9 48.4 77.4 81.5 0.8 1,911 (31.8)2,837 (47.2)1.3
I. Papakis, A. Sarkar, A. Karpatne. GCNNMatch: Graph Convolutional Neural Networks for Multi-Object Tracking via Sinkhorn Normalization. In arXiv preprint arXiv:2010.00067, 2020.
TG_CR
37. using public detections 		57.1 59.3 47.5 554 (23.5)823 (34.9)15,209 224,841 60.2 95.7 49.4 46.0 53.9 77.2 48.5 77.2 81.3 0.9 1,766 (0.0)2,349 (0.0)8.9
C. Ma, F. Yang, Y. Li, H. Jia, X. Xie, W. Gao. Deep trajectory post-processing and position projection for single & multiple camera multiple object tracking. In International Journal of Computer Vision, 2021.
UNS20regress
38. online method using public detections 		56.8 58.3 46.4 538 (22.8)880 (37.4)11,567 230,645 59.1 96.6 47.9 45.3 51.0 79.0 47.6 77.8 81.4 0.7 1,320 (22.3)2,061 (34.9)72.8
F. Bastani, S. He, S. Madden. Self-Supervised Multi-Object Tracking with Cross-input Consistency. In Advances in Neural Information Processing Systems (NeurIPS), 2021.
GSM_Tracktor
39. online method using public detections 		56.4 57.8 45.7 523 (22.2)813 (34.5)14,379 230,174 59.2 95.9 47.0 44.9 50.4 77.4 47.3 76.7 80.9 0.8 1,485 (25.1)2,763 (46.7)8.7
Q. Liu, Q. Chu, B. Liu, N. Yu. Gsm: graph similarity model for multi-object tracking. In International Joint Conferences on Artificial Intelligence Organization, 2020.
Tracktor++v2
40. online method using public detections 		56.3 55.1 44.8 498 (21.1)831 (35.3)8,866 235,449 58.3 97.4 45.1 44.9 48.3 78.4 47.0 78.6 81.8 0.5 1,987 (34.1)3,763 (64.6)1.5
P. Bergmann, T. Meinhardt, L. Leal-Taixé. Tracking without bells and whistles. In ICCV, 2019.
TrackerMOTAIDF1HOTAMTMLFPFNRcllPrcnAssADetAAssReAssPrDetReDetPrLocAFAFID Sw.FragHz
MOMOT_GLMB
41. online method using public detections 		55.5 63.4 48.1 448 (19.0)845 (35.9)15,520 234,235 58.5 95.5 52.3 44.5 56.5 77.3 46.9 76.6 80.9 0.9 1,333 (22.8)13,805 (236.0)0.6
Mohammadjavad Abbaspour, Mohammad Ali Masnadi-Shirazi. Online Multi-Object Tracking with delta-GLMB Filter based on Occlusion and Identity Switch Handling. In arXiv preprint arXiv:2011.10111, 2020.
GMOT
42. using public detections 		55.4 57.9 45.3 535 (22.7)817 (34.7)20,608 229,511 59.3 94.2 46.6 44.4 51.8 71.3 47.2 74.9 79.9 1.2 1,403 (23.7)2,765 (46.6)5.9
LXD, KHW @ HRI-SH
TT17
43. using public detections 		54.9 63.1 48.4 575 (24.4)897 (38.1)20,236 233,295 58.7 94.2 53.2 44.3 56.7 78.2 47.0 75.5 80.3 1.1 1,088 (18.6)2,392 (40.8)2.5
Y. Zhang, H. Sheng, Y. Wu, S. Wang, W. Lyu, W. Ke, Z. Xiong. Long-term Tracking with Deep Tracklet Association. In IEEE Transactions on Image Processing, 2020.
LSST17
44. using public detections 		54.7 62.3 47.1 480 (20.4)944 (40.1)26,091 228,434 59.5 92.8 51.0 43.8 56.0 72.5 46.9 73.1 79.3 1.5 1,243 (20.9)3,726 (62.6)1.5
Multi-Object Tracking with Multiple Cues and Switcher-Aware Classification
TADN
45. online method using public detections 		54.6 49.0 39.7 528 (22.4)711 (30.2)36,285 214,857 61.9 90.6 35.1 45.3 43.1 59.3 49.4 72.2 80.1 2.0 4,869 (0.0)7,821 (0.0)360.9
A. Psalta, V. Tsironis, K. Karantzalos. Transformer-based assignment decision network for multiple object tracking. In , 2022.
DGCT
46. using public detections 		54.5 51.3 42.0 495 (21.0)834 (35.4)10,471 243,143 56.9 96.8 40.5 43.9 44.1 73.8 46.0 78.4 81.8 0.6 2,865 (50.3)4,889 (85.9)7.0
CJY, HYW, KHW @ HRI-SH
ISDH_HDAv2
47. online method using public detections 		54.5 65.9 49.2 622 (26.4)755 (32.1)46,693 207,093 63.3 88.4 53.5 45.5 57.3 75.0 50.0 69.9 78.5 2.6 3,010 (47.6)6,000 (94.8)3.6
MM-008988/ IEEE Transactions on Multimedia
TPM
48. using public detections 		54.2 52.6 41.5 536 (22.8)882 (37.5)13,739 242,730 57.0 95.9 40.9 42.5 44.8 71.7 44.8 75.5 80.0 0.8 1,824 (32.0)2,472 (43.4)0.8
J. Peng, T. Wang, et.al. TPM: Multiple Object Tracking with Tracklet-Plane Matching. In Pattern Recognition, 2020.
HDTR
49. using public detections 		54.1 48.4 46.8 549 (23.3)819 (34.8)18,002 238,818 57.7 94.8 48.9 44.9 54.3 74.1 47.6 78.2 82.6 1.0 1,895 (32.9)2,693 (46.7)1.8
M. Babaee, A. Athar, G. Rigoll. Multiple People Tracking Using Hierarchical Deep Tracklet Re-identification. In arXiv preprint arXiv:1811.04091, 2018.
TrctrD17
50. online method using public detections 		53.7 53.8 42.4 458 (19.4)861 (36.6)11,731 247,447 56.1 96.4 42.7 42.5 46.8 73.4 44.6 76.6 80.4 0.7 1,947 (34.7)4,792 (85.4)4.9
Y. Xu, A. Osep, Y. Ban, R. Horaud, L. Leal-Taixe, X. Alameda-Pineda. How To Train Your Deep Multi-Object Tracker. In , 2019.
TrackerMOTAIDF1HOTAMTMLFPFNRcllPrcnAssADetAAssReAssPrDetReDetPrLocAFAFID Sw.FragHz
Tracktor++
51. online method using public detections 		53.5 52.3 42.1 459 (19.5)861 (36.6)12,201 248,047 56.0 96.3 41.7 42.9 45.4 75.6 45.0 77.3 80.9 0.7 2,072 (37.0)4,611 (82.3)1.5
P. Bergmann, T. Meinhardt, L. Leal-Taixé. Tracking without bells and whistles. In ICCV, 2019.
SFS
52. online method using public detections 		53.1 53.4 41.5 471 (20.0)807 (34.3)37,802 223,761 60.3 90.0 40.4 43.0 44.2 69.8 46.8 69.7 77.9 2.1 2,806 (46.5)6,380 (105.7)0.9
MTSFS： Online Multi-Object Tracking Based on Salient Feature Selection in Crowded Scenes
ENFT17
53. using public detections 		52.8 57.1 44.5 543 (23.1)867 (36.8)26,754 237,909 57.8 92.4 46.2 43.2 50.6 74.0 46.3 74.0 80.4 1.5 1,667 (28.8)2,557 (44.2)0.5
BUAA
LSST17O
54. online method using public detections 		52.7 57.9 44.3 421 (17.9)863 (36.6)22,512 241,936 57.1 93.5 46.7 42.4 50.7 73.2 45.1 73.8 79.5 1.3 2,167 (37.9)7,443 (130.3)1.8
Multi-Object Tracking with Multiple Cues and Switcher-Aware Classification
JBNOT
55. using public detections 		52.6 50.8 41.3 465 (19.7)844 (35.8)31,572 232,659 58.8 91.3 39.8 43.3 43.0 73.9 46.9 72.8 80.2 1.8 3,050 (51.9)3,792 (64.5)5.4
R. Henschel, Y. Zou, B. Rosenhahn. Multiple People Tracking using Body and Joint Detections. In CVPRW, 2019.
FAMNet
56. online method using public detections 		52.0 48.7 0.0 450 (19.1)787 (33.4)14,138 253,616 55.1 95.6 0.0 0.0 0.0 0.0 0.0 0.0 100.0 0.8 3,072 (55.8)5,318 (96.6)0.0
P. Chu, H. Ling. FAMNet: Joint Learning of Feature, Affinity and Multi-dimensional Assignment for Online Multiple Object Tracking. In ICCV, 2019.
eTC17
57. using public detections 		51.9 58.1 44.9 544 (23.1)836 (35.5)36,164 232,783 58.7 90.2 47.0 43.3 51.7 72.1 46.9 72.0 79.4 2.0 2,288 (38.9)3,071 (52.3)0.7
G. Wang, Y. Wang, H. Zhang, R. Gu, J. Hwang. Exploit the connectivity: Multi-object tracking with trackletnet. In Proceedings of the 27th ACM International Conference on Multimedia, 2019.
CMT
58. using public detections 		51.8 60.7 46.9 462 (19.6)1,009 (42.8)29,528 240,960 57.3 91.6 51.6 42.8 55.5 77.1 46.0 73.6 80.3 1.7 1,217 (21.2)2,008 (35.0)6.5
#Submission: TIP-21190-2019
eHAF17
59. using public detections 		51.8 54.7 43.4 551 (23.4)893 (37.9)33,212 236,772 58.0 90.8 44.3 42.8 49.7 70.1 46.3 72.5 80.0 1.9 1,834 (31.6)2,739 (47.2)0.7
H. Sheng, Y. Zhang, J. Chen, Z. Xiong, J. Zhang. Heterogeneous Association Graph Fusion for Target Association in Multiple Object Tracking. In IEEE Transactions on Circuits and Systems for Video Technology, 2018.
AFN17
60. using public detections 		51.5 46.9 38.5 485 (20.6)836 (35.5)22,391 248,420 56.0 93.4 35.6 42.1 37.6 77.8 44.8 74.8 80.6 1.3 2,593 (46.3)4,308 (77.0)1.8
H. Shen, L. Huang, C. Huang, W. Xu. Tracklet Association Tracker: An End-to-End Learning-based Association Approach for Multi-Object Tracking. In CoRR, 2018.
TrackerMOTAIDF1HOTAMTMLFPFNRcllPrcnAssADetAAssReAssPrDetReDetPrLocAFAFID Sw.FragHz
BLSTM_MTP_O
61. online method using public detections 		51.5 54.9 41.3 481 (20.4)836 (35.5)29,616 241,619 57.2 91.6 41.6 41.3 44.8 72.0 44.3 70.9 77.9 1.7 2,566 (0.0)7,748 (0.0)20.1
C. Kim, F. Li, M. Alotaibi, J. Rehg. Discriminative Appearance Modeling with Multi-track Pooling for Real-time Multi-object Tracking. In CVPR (accepted), 2021.
CoCT_pub
62. online method using public detections 		51.4 55.2 42.6 478 (20.3)913 (38.8)27,164 245,236 56.5 92.2 43.6 41.9 49.2 69.7 44.9 73.2 79.8 1.5 1,657 (0.0)3,987 (0.0)29.6
https://ieeexplore.ieee.org/abstract/document/9247175
YOONKJ17
63. online method using public detections 		51.4 54.0 36.9 500 (21.2)878 (37.3)29,051 243,202 56.9 91.7 43.5 31.4 48.5 70.8 33.2 72.8 79.8 1.6 2,118 (37.2)3,072 (54.0)3.4
K. YOON, J. GWAK, Y. SONG, Y. YOON, M. JEON. OneShotDA: Online Multi-object Tracker with One-shot-learning-based Data Association. In IEEE Access, 2020.
FWT
64. using public detections 		51.3 47.6 39.0 505 (21.4)830 (35.2)24,101 247,921 56.1 92.9 36.8 41.9 39.8 72.3 44.7 74.0 80.0 1.4 2,648 (47.2)4,279 (76.3)0.2
R. Henschel, L. Leal-Taixé, D. Cremers, B. Rosenhahn. Fusion of Head and Full-Body Detectors for Multi-Object Tracking. In Trajnet CVPRW, 2018.
NOTA
65. using public detections 		51.3 54.5 42.3 403 (17.1)833 (35.4)20,148 252,531 55.2 93.9 43.6 41.3 47.6 72.8 43.8 74.5 79.9 1.1 2,285 (41.4)5,798 (105.0)17.8
L. Chen, H. Ai, R. Chen, Z. Zhuang. Aggregate Tracklet Appearance Features for Multi-Object Tracking. In IEEE Signal Processing Letters, 2019.
jCC
66. using public detections 		51.2 54.5 42.5 493 (20.9)872 (37.0)25,937 247,822 56.1 92.4 44.2 41.2 46.7 77.9 44.0 72.6 79.2 1.5 1,802 (32.1)2,984 (53.2)1.8
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.
STRN_MOT17
67. online method using public detections 		50.9 56.0 42.6 446 (18.9)797 (33.8)25,295 249,365 55.8 92.6 44.2 41.2 47.9 73.2 43.9 72.9 79.0 1.4 2,397 (43.0)9,363 (167.8)13.8
J. Xu, Y. Cao, Z. Zhang, H. Hu. Spatial-Temporal Relation Networks for Multi-Object Tracking. In ICCV, 2019.
MOTDT17
68. online method using public detections 		50.9 52.7 41.2 413 (17.5)841 (35.7)24,069 250,768 55.6 92.9 41.4 41.3 45.6 70.3 44.1 73.7 79.7 1.4 2,474 (44.5)5,317 (95.7)18.3
C. Long, A. Haizhou, Z. Zijie, S. Chong. Real-time Multiple People Tracking with Deeply Learned Candidate Selection and Person Re-identification. In ICME, 2018.
MHT_DAM
69. using public detections 		50.7 47.2 38.9 491 (20.8)869 (36.9)22,875 252,889 55.2 93.2 36.7 41.6 38.3 80.2 44.3 74.8 80.6 1.3 2,314 (41.9)2,865 (51.9)0.9
C. Kim, F. Li, A. Ciptadi, J. Rehg. Multiple Hypothesis Tracking Revisited. In ICCV, 2015.
TLMHT
70. using public detections 		50.6 56.5 44.2 415 (17.6)1,022 (43.4)22,213 255,030 54.8 93.3 47.5 41.4 50.7 78.0 44.0 75.0 80.6 1.3 1,407 (25.7)2,079 (37.9)2.6
H. Sheng, J. Chen, Y. Zhang, W. Ke, Z. Xiong, J. Yu. Iterative Multiple Hypothesis Tracking with Tracklet-level Association. In IEEE Transactions on Circuits and Systems for Video Technology, 2018.
TrackerMOTAIDF1HOTAMTMLFPFNRcllPrcnAssADetAAssReAssPrDetReDetPrLocAFAFID Sw.FragHz
DEEP_TAMA
71. online method using public detections 		50.3 53.5 42.0 453 (19.2)883 (37.5)25,479 252,996 55.2 92.4 43.3 41.0 46.9 73.1 43.8 73.4 79.7 1.4 2,192 (39.7)3,978 (72.1)1.5
Y. Yoon, D. Kim, Y. Song, K. Yoon, M. Jeon. Online Multiple Pedestrians Tracking using Deep Temporal Appearance Matching Association. In Information Sciences, 2020.
EDMT17
72. using public detections 		50.0 51.3 41.3 509 (21.6)855 (36.3)32,279 247,297 56.2 90.8 41.1 41.8 43.4 77.8 45.1 72.9 80.2 1.8 2,264 (40.3)3,260 (58.0)0.6
J. Chen, H. Sheng, Y. Zhang, Z. Xiong. Enhancing Detection Model for Multiple Hypothesis Tracking. In BMTT-PETS CVPRw, 2017.
GMPHDOGM17
73. online method using public detections 		49.9 47.1 0.0 464 (19.7)895 (38.0)24,024 255,277 54.8 92.8 0.0 0.0 0.0 0.0 0.0 0.0 100.0 1.4 3,125 (57.1)3,540 (64.7)30.7
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.
CS_MOT
74. online method using public detections 		49.7 51.5 39.0 300 (12.7)899 (38.2)25,149 256,074 54.6 92.5 38.7 39.6 41.9 71.4 42.3 71.5 78.2 1.4 2,499 (45.8)10,312 (188.8)1.1
A Cost Matrix Optimization Method Based on Spatial Constraints under Hungarian Algorithm
MTDF17
75. online method using public detections 		49.6 45.2 37.7 444 (18.9)779 (33.1)37,124 241,768 57.2 89.7 34.5 42.0 36.5 76.2 45.4 71.3 78.8 2.1 5,567 (97.4)9,260 (162.0)1.2
Z. Fu, F. Angelini, J. Chambers, S. Naqvi. Multi-Level Cooperative Fusion of GM-PHD Filters for Online Multiple Human Tracking. In IEEE Transactions on Multimedia, 2019.
DASOT17
76. online method using public detections 		49.5 51.8 41.5 481 (20.4)814 (34.6)33,640 247,370 56.2 90.4 41.7 41.6 44.4 75.6 44.9 72.3 79.9 1.9 4,142 (73.8)6,852 (122.0)9.1
Q. Chu, W. Ouyang, B. Liu, F. Zhu, N. Yu. DASOT: A Unified Framework Integrating Data Association and Single Object Tracking for Online Multi-Object Tracking. In Proceedings of the AAAI Conference on Artificial Intelligence, 2020.
PHD_GM
77. online method using public detections 		48.8 43.2 0.0 449 (19.1)830 (35.2)26,260 257,971 54.3 92.1 0.0 0.0 0.0 0.0 0.0 0.0 100.0 1.5 4,407 (81.2)6,448 (118.8)22.3
R. Sanchez-Matilla, A. Cavallaro. Motion Prediction for First-person Vision Multi-object Tracking. In ECCVw, 2020.
OTCD_1
78. online method using public detections 		48.6 47.9 38.4 382 (16.2)970 (41.2)18,499 268,204 52.5 94.1 37.6 39.6 40.4 73.8 41.8 74.9 80.0 1.0 3,502 (66.7)5,588 (106.5)15.5
Q. Liu, B. Liu, Y. Wu, W. Li, N. Yu. Real-Time Online Multi-Object Tracking in Compressed Domain. In IEEE Access, 2019.
HAM_SADF17
79. online method using public detections 		48.3 51.1 40.3 402 (17.1)981 (41.7)20,967 269,038 52.3 93.4 41.5 39.3 44.4 75.8 41.7 74.5 80.2 1.2 1,871 (35.8)3,020 (57.7)5.0
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.
DMAN
80. online method using public detections 		48.2 55.7 42.5 454 (19.3)902 (38.3)26,218 263,608 53.3 92.0 46.1 39.4 49.9 72.9 42.0 72.5 79.0 1.5 2,194 (41.2)5,378 (100.9)0.3
J. Zhu, H. Yang, N. Liu, M. Kim, W. Zhang, M. Yang. Online Multi-Object Tracking with Dual Matching Attention Networks. In ECCV, 2018.
TrackerMOTAIDF1HOTAMTMLFPFNRcllPrcnAssADetAAssReAssPrDetReDetPrLocAFAFID Sw.FragHz
AM_ADM17
81. online method using public detections 		48.1 52.1 40.5 316 (13.4)934 (39.7)25,061 265,495 52.9 92.3 42.0 39.5 45.1 73.8 42.1 73.4 79.8 1.4 2,214 (41.8)5,027 (94.9)5.7
S. Lee, M. Kim, S. Bae, Learning Discriminative Appearance Models for Online Multi-Object Tracking with Appearance Discriminability Measures, In IEEE Access, 2018.
PHD_GSDL17
82. online method using public detections 		48.0 49.6 39.2 402 (17.1)838 (35.6)23,199 265,954 52.9 92.8 39.2 39.5 43.4 70.2 42.1 73.9 80.1 1.3 3,998 (75.6)8,886 (168.1)6.7
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.
BBT
83. using public detections 		47.8 50.0 40.0 449 (19.1)936 (39.7)35,552 255,118 54.8 89.7 40.3 40.0 45.3 66.8 43.3 71.0 79.2 2.0 3,676 (67.1)4,949 (90.3)121.6
MTAP-D-20-01870 Tracking Subjects and Detecting Relationships in Crowded City Videos (under review)
MHT_bLSTM
84. using public detections 		47.5 51.9 41.0 429 (18.2)981 (41.7)25,981 268,042 52.5 91.9 42.8 39.5 47.3 73.8 42.2 73.8 80.4 1.5 2,069 (39.4)3,124 (59.5)1.9
C. Kim, F. Li, J. Rehg. Multi-object Tracking with Neural Gating Using Bilinear LSTM. In ECCV, 2018.
DAM_MOT
85. online method using public detections 		47.0 48.7 0.0 397 (16.9)897 (38.1)28,933 267,896 52.5 91.1 0.0 0.0 0.0 0.0 0.0 0.0 100.0 1.6 2,140 (40.7)2,756 (52.5)18.7
Multi Object Tracking using Deep Structural Cost Minimization in Data Association
MASS
86. online method using public detections 		46.9 46.0 36.8 399 (16.9)856 (36.3)25,733 269,116 52.3 92.0 35.4 38.5 38.8 70.2 41.2 72.4 79.4 1.4 4,478 (85.6)11,994 (229.3)17.1
H. Karunasekera, H. Wang, H. Zhang. Multiple Object Tracking With Attention to Appearance, Structure, Motion and Size. In IEEE Access, 2019.
GMPHD_Rd17
87. online method using public detections 		46.8 54.1 41.5 464 (19.7)784 (33.3)38,452 257,678 54.3 88.9 43.5 39.9 47.2 75.0 43.3 70.9 79.5 2.2 3,865 (71.1)8,097 (149.0)30.8
N. Baisa. Occlusion-robust online multi-object visual tracking using a GM-PHD filter with CNN-based re-identification. In Journal of Visual Communication and Image Representation, 2021.
PHD_DCM
88. online method using public detections 		46.5 47.6 38.2 397 (16.9)875 (37.2)23,859 272,430 51.7 92.4 37.9 38.8 41.1 73.0 41.3 73.8 80.2 1.3 5,649 (109.2)9,298 (179.8)1.6
Z. Fu, F. Angelini, S. Naqvi, J. Chambers. GM-PHD Filter Based Online Multiple Human Tracking Using Deep Discriminative Correlation Matching. In 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2018.
PHD_LMP
89. online method using public detections 		45.9 42.5 0.0 364 (15.5)892 (37.9)27,946 272,196 51.8 91.3 0.0 0.0 0.0 0.0 0.0 0.0 100.0 1.6 4,977 (96.2)6,985 (135.0)29.4
R. Sanchez-Matilla, A. Cavallaro. Motion Prediction for First-person Vision Multi-object Tracking. In ECCVw, 2020.
EDA_GNN
90. online method using public detections 		45.5 40.5 33.6 368 (15.6)955 (40.6)25,685 277,663 50.8 91.8 30.3 37.8 32.2 72.9 40.3 72.8 79.5 1.4 4,091 (80.5)5,579 (109.8)39.3
Paper ID 2713
TrackerMOTAIDF1HOTAMTMLFPFNRcllPrcnAssADetAAssReAssPrDetReDetPrLocAFAFID Sw.FragHz
IOU17
91. using public detections 		45.5 39.4 33.5 369 (15.7)953 (40.5)19,993 281,643 50.1 93.4 30.4 37.4 32.9 73.6 39.6 73.8 79.9 1.1 5,988 (119.6)7,404 (147.8)1,522.9
E. Bochinski, V. Eiselein, T. Sikora. High-Speed Tracking-by-Detection Without Using Image Information. In International Workshop on Traffic and Street Surveillance for Safety and Security at IEEE AVSS 2017, 2017.
HISP_DAL17
92. online method using public detections 		45.4 39.9 34.0 349 (14.8)922 (39.2)21,820 277,473 50.8 92.9 30.7 38.1 32.2 77.3 40.5 74.1 80.2 1.2 8,727 (171.7)7,147 (140.6)3.2
N. Baisa. Robust online multi-target visual tracking using a HISP filter with discriminative deep appearance learning. In Journal of Visual Communication and Image Representation, 2021.
LM_NN
93. using public detections 		45.1 43.2 0.0 348 (14.8)1,088 (46.2)10,834 296,451 47.5 96.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.6 2,286 (48.2)2,463 (51.9)0.9
M. Babaee, Z. Li, G. Rigoll. A Dual CNN--RNN for Multiple People Tracking. In Neurocomputing, 2019.
FPSN
94. online method using public detections 		44.9 48.4 38.1 388 (16.5)844 (35.8)33,757 269,952 52.2 89.7 38.3 38.3 41.7 70.3 41.4 71.2 79.6 1.9 7,136 (136.8)14,491 (277.8)10.1
S. Lee, E. Kim. Multiple Object Tracking via Feature Pyramid Siamese Networks. In IEEE ACCESS, 2018.
HISP_T17
95. online method using public detections 		44.6 38.8 33.3 355 (15.1)913 (38.8)25,478 276,395 51.0 91.9 29.5 38.1 31.1 76.3 40.7 73.2 80.1 1.4 10,617 (208.1)7,487 (146.8)4.7
N. Baisa. Online Multi-target Visual Tracking using a HISP Filter. In Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5: VISAPP,, 2018.
GMPHD_DAL
96. online method using public detections 		44.4 36.2 31.4 350 (14.9)927 (39.4)19,170 283,380 49.8 93.6 26.6 37.5 29.1 69.7 39.7 74.6 80.4 1.1 11,137 (223.7)13,900 (279.3)3.4
N. Baisa. Online Multi-object Visual Tracking using a GM-PHD Filter with Deep Appearance Learning. In 2019 22th International Conference on Information Fusion (FUSION), 2019.
SAS_MOT17
97. using public detections 		44.2 57.2 43.0 379 (16.1)1,044 (44.3)29,473 283,611 49.7 90.5 50.1 37.0 53.7 76.3 39.7 72.2 79.5 1.7 1,529 (30.7)2,644 (53.2)4.8
A. Maksai, P. Fua. Eliminating Exposure Bias and Metric Mismatch in Multiple Object Tracking. In CVPR, 2019.
GMPHD_SHA
98. online method using public detections 		43.7 39.2 33.2 276 (11.7)1,012 (43.0)25,935 287,758 49.0 91.4 30.6 36.6 32.3 77.3 39.0 72.7 79.6 1.5 3,838 (78.3)5,056 (103.2)9.2
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.
SORT17
99. online method using public detections 		43.1 39.8 34.0 295 (12.5)997 (42.3)28,398 287,582 49.0 90.7 31.8 37.0 33.1 79.6 39.5 73.1 80.5 1.6 4,852 (99.0)7,127 (145.4)143.3
A. Bewley, Z. Ge, L. Ott, F. Ramos, B. Upcroft. Simple online and realtime tracking. In 2016 IEEE International Conference on Image Processing (ICIP), 2016.
EAMTT
100. online method using public detections 		42.6 41.8 0.0 300 (12.7)1,006 (42.7)30,711 288,474 48.9 90.0 0.0 0.0 0.0 0.0 0.0 0.0 100.0 1.7 4,488 (91.8)5,720 (117.0)12.0
R. Sanchez-Matilla, F. Poiesi, A. Cavallaro. Online Multi-target Tracking with Strong and Weak Detections. In Computer Vision -- ECCV 2016 Workshops, 2016.
TrackerMOTAIDF1HOTAMTMLFPFNRcllPrcnAssADetAAssReAssPrDetReDetPrLocAFAFID Sw.FragHz
GMPHD_N1Tr
101. online method using public detections 		42.1 33.9 30.3 280 (11.9)1,005 (42.7)18,214 297,646 47.2 93.6 25.9 36.1 27.3 76.2 38.0 75.2 80.5 1.0 10,698 (226.4)10,864 (229.9)9.9
N. Baisa, A. Wallace. Development of a N-type GM-PHD filter for multiple target, multiple type visual tracking. In Journal of Visual Communication and Image Representation, 2019.
FlowTracker
102. online method using public detections 		40.4 38.0 32.5 330 (14.0)863 (36.6)60,962 269,136 52.3 82.9 28.9 37.1 30.4 76.8 41.6 66.0 78.8 3.4 5,927 (0.0)6,748 (0.0)61.2
H. Nishimura et al., "SDOF-Tracker: Fast and Accurate Multiple Human Tracking by Skipped-Detection and Optical-Flow"
OVBT17
103. online method using public detections 		40.2 43.6 34.6 151 (6.4)1,169 (49.6)27,497 305,179 45.9 90.4 34.9 34.7 36.9 75.3 36.9 72.7 80.1 1.5 4,763 (0.0)8,711 (0.0)1.7
Y. Ban, S. Ba, X. Alameda-Pineda, R. Horaud. Tracking multiple persons based on a variational bayesian model. In European Conference on Computer Vision Workshops, 2016.
GMPHD_KCF
104. online method using public detections 		39.6 36.6 30.3 208 (8.8)1,019 (43.3)50,903 284,228 49.6 84.6 26.4 35.3 28.0 72.8 38.9 66.3 77.8 2.9 5,811 (117.1)7,414 (149.4)3.3
T. Kutschbach, E. Bochinski, V. Eiselein, T. Sikora. Sequential Sensor Fusion Combining Probability Hypothesis Density and Kernelized Correlation Filters for Multi-Object Tracking in Video Data. In International Workshop on Traffic and Street Surveillance for Safety and Security at IEEE AVSS 2017, 2017.
GM_PHD
105. online method using public detections 		36.4 33.9 28.1 97 (4.1)1,349 (57.3)23,723 330,767 41.4 90.8 26.0 30.8 27.1 77.7 32.7 71.7 79.4 1.3 4,607 (111.3)11,317 (273.5)38.4
V. Eiselein, D. Arp, M. Pätzold, T. Sikora. Real-time Multi-Human Tracking using a Probability Hypothesis Density Filter and multiple detectors. In 9th IEEE International Conference on Advanced Video and Signal-Based Surveillance, 2012.
MOTR_test
106. online method using public detections 		34.4 51.7 43.8 612 (26.0)794 (33.7)172,571 194,478 65.5 68.2 46.9 41.2 51.6 75.1 54.6 56.8 81.8 9.7 2,824 (0.0)4,629 (0.0)66.9
None
SequencesFramesTrajectoriesBoxes
21177572355564228

Evaluation Measures

Lower is better. Higher is better.
Measure Better Perfect Description
MOTA higher 100%Multi-Object Tracking Accuracy (+/- denotes standard deviation across all sequences) [1]. This measure combines three error sources: false positives, missed targets and identity switches.
IDF1 higher 100%ID F1 Score [2]. The ratio of correctly identified detections over the average number of ground-truth and computed detections.
HOTA higher 100%Higher Order Tracking Accuracy [3]. Geometric mean of detection accuracy and association accuracy. Averaged across localization thresholds.
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 0The total number of false positives.
FN lower 0The total number of false negatives (missed targets).
Rcll higher 100%Ratio of correct detections to total number of GT boxes.
Prcn higher 100%Ratio of TP / (TP+FP).
AssA higher 100%Association Accuracy [3]. Association Jaccard index averaged over all matching detections and then averaged over localization thresholds.
DetA higher 100%Detection Accuracy [3]. Detection Jaccard index averaged over localization thresholds.
AssRe higher 100%Association Recall [3]. TPA / (TPA + FNA) averaged over all matching detections and then averaged over localization thresholds.
AssPr higher 100%Association Precision [3]. TPA / (TPA + FPA) averaged over all matching detections and then averaged over localization thresholds.
DetRe higher 100%Detection Recall [3]. TP /(TP + FN) averaged over localization thresholds.
DetPr higher 100%Detection Precision [3]. TP /(TP + FP) averaged over localization thresholds.
LocA higher 100%Localization Accuracy [3]. Average localization similarity averaged over all matching detections and averaged over localization thresholds.
FAF lower 0The average number of false alarms per frame.
ID Sw. lower 0Number of Identity Switches (ID switch ratio = #ID switches / recall) [4]. Please note that we follow the stricter definition of identity switches as described in the reference
Frag lower 0The 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. The frequency is provided by the authors and not officially evaluated by the MOTChallenge.

Legend

Symbol Description
online method 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.
using public detections This method used the provided detection set as input.
using private detections This method used a private detection set as input.
new This entry has been submitted or updated less than a week ago.

References:


[1] Bernardin, K. & Stiefelhagen, R. Evaluating Multiple Object Tracking Performance: The CLEAR MOT Metrics. Image and Video Processing, 2008(1):1-10, 2008.
[2] Ristani, E., Solera, F., Zou, R., Cucchiara, R. & Tomasi, C. Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking. In ECCV workshop on Benchmarking Multi-Target Tracking, 2016.
[3] Jonathon Luiten, A.O. & Leibe, B. HOTA: A Higher Order Metric for Evaluating Multi-Object Tracking. International Journal of Computer Vision, 2020.
[4] Li, Y., Huang, C. & Nevatia, R. 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.



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