MOT20 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
GMOTv2
1. using public detections 		77.1 74.2 61.5 895 (72.1)117 (9.4)43,118 73,849 85.7 91.1 59.3 63.9 66.5 72.2 71.7 76.2 83.7 9.6 1,687 (0.0)1,884 (0.0)0.9
BOETrackerV2
2. online method using public detections 		74.5 73.7 60.8 792 (63.8)163 (13.1)18,070 112,717 78.2 95.7 60.6 61.3 65.9 78.3 65.7 80.4 84.2 4.0 1,401 (0.0)1,891 (0.0)14.9
STC_pub
3. online method using public detections 		73.0 67.6 56.1 833 (67.1)147 (11.8)30,880 106,876 79.3 93.0 52.9 59.8 59.5 72.7 65.6 76.9 83.2 6.9 2,172 (0.0)3,313 (0.0)1.4
MrMOT
4. online method using public detections 		67.7 67.8 53.9 689 (55.5)162 (13.0)32,536 131,330 74.6 92.2 52.9 55.1 58.8 71.7 60.5 74.7 81.7 7.3 3,176 (0.0)8,874 (0.0)16.9
kalman_pub
5. online method using public detections 		67.0 70.2 56.4 592 (47.7)263 (21.2)9,685 160,303 69.0 97.4 58.3 54.8 65.0 73.6 57.7 81.4 84.1 2.2 680 (0.0)1,738 (0.0)17.7
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.
OUTrack_fm_p
6. online method using public detections 		65.4 65.1 52.1 615 (49.5)165 (13.3)38,243 137,770 73.4 90.8 50.7 53.8 53.9 77.8 59.4 73.6 81.4 8.5 2,885 (0.0)7,205 (0.0)5.1
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.
GGDA
7. using public detections 		64.4 65.7 53.4 746 (60.1)143 (11.5)70,976 110,614 78.6 85.1 52.4 54.7 58.1 70.8 64.0 69.3 81.6 15.8 2,708 (0.0)2,354 (0.0)0.4
GNMOT_IA
8. using public detections 		63.1 65.2 52.5 761 (61.3)131 (10.5)71,152 115,701 77.6 85.0 51.3 54.1 56.8 70.6 63.2 69.2 81.5 15.9 4,041 (0.0)13,101 (0.0)3.6
RETracker
9. online method using public detections 		62.4 53.0 45.3 605 (48.7)192 (15.5)43,503 147,451 71.5 89.5 38.6 53.4 53.3 52.3 59.3 74.2 82.1 9.7 3,804 (0.0)9,245 (0.0)0.0
Y. Kawanishi. Label-Based Multiple Object Ensemble Tracking with Randomized Frame Dropping. In Proceedings of the 26th International Conference on Pattern Recognition, 2022.
TransCtr
10. online method using public detections 		61.0 49.8 43.5 601 (48.4)192 (15.5)49,189 147,890 71.4 88.3 36.1 52.9 44.5 59.0 59.2 73.1 81.7 11.0 4,493 (0.0)8,950 (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.
TrackerMOTAIDF1HOTAMTMLFPFNRcllPrcnAssADetAAssReAssPrDetReDetPrLocAFAFID Sw.FragHz
GATracker
11. using public detections 		60.7 61.6 50.6 603 (48.6)187 (15.1)48,356 147,468 71.5 88.4 48.2 53.4 51.1 78.8 59.5 73.6 81.9 10.8 7,548 (0.0)9,215 (0.0)2.2
MPTC
12. online method using public detections 		60.6 59.7 48.5 635 (51.1)208 (16.7)45,318 153,978 70.2 88.9 46.5 50.7 51.6 66.4 56.8 72.0 81.4 10.1 4,533 (64.5)5,163 (73.5)0.7
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
13. online method using public detections 		60.1 61.2 48.9 580 (46.7)221 (17.8)38,043 165,899 67.9 90.2 48.4 49.7 52.9 71.0 54.8 72.9 81.2 8.5 2,342 (34.5)4,320 (63.6)0.6
D. Stadler, J. Beyerer. Improving Multiple Pedestrian Tracking by Track Management and Occlusion Handling. In CVPR, 2021.
STTMOT
14. online method using public detections 		60.1 66.7 51.7 578 (46.5)221 (17.8)37,657 165,866 67.9 90.3 53.6 50.0 57.7 75.9 55.1 73.2 81.4 8.4 2,926 (0.0)4,451 (0.0)4.0
OCSORTpublic
15. online method using public detections 		59.9 67.0 54.3 478 (38.5)330 (26.6)4,434 202,502 60.9 98.6 59.5 49.7 65.1 76.6 51.6 83.5 85.0 1.0 554 (0.0)2,345 (0.0)27.6
J. Cao, X. Weng, R. Khirodkar, J. Pang, K. Kitani. Observation-Centric SORT: Rethinking SORT for Robust Multi-Object Tracking. In , 2022.
xumot
16. using public detections 		59.7 67.8 54.0 826 (66.5)96 (7.7)106,415 96,686 81.3 79.8 55.4 53.0 60.9 72.8 65.6 64.4 80.7 23.8 5,671 (0.0)8,154 (0.0)11.9
mfi_tst
17. online method using public detections 		59.3 59.1 47.1 511 (41.1)215 (17.3)36,150 172,782 66.6 90.5 46.2 48.4 50.0 73.9 53.1 72.2 79.9 8.1 1,919 (0.0)3,743 (0.0)0.5
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.
MOTer
18. online method using public detections 		59.1 49.9 43.6 598 (48.1)189 (15.2)59,624 147,333 71.5 86.1 37.0 51.8 45.2 58.8 58.9 71.0 81.2 13.3 4,597 (0.0)9,232 (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.
STRTrack
19. online method using public detections 		59.0 67.1 53.6 849 (68.4)94 (7.6)114,945 92,089 82.2 78.7 54.6 52.8 60.4 71.7 66.2 63.4 80.6 25.7 5,048 (0.0)6,009 (0.0)16.9
ApLift
20. using public detections 		58.9 56.5 46.6 513 (41.3)264 (21.3)17,739 192,736 62.8 94.8 45.2 48.2 48.1 76.8 51.3 77.5 82.2 4.0 2,241 (0.0)2,112 (0.0)0.4
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.
TrackerMOTAIDF1HOTAMTMLFPFNRcllPrcnAssADetAAssReAssPrDetReDetPrLocAFAFID Sw.FragHz
MPNTrack
21. using public detections 		57.6 59.1 46.8 474 (38.2)279 (22.5)16,953 201,384 61.1 94.9 47.3 46.6 52.7 70.0 49.5 76.9 81.6 3.8 1,210 (19.8)1,420 (23.2)6.5
G. Braso, L. Leal-Taixe. Learning a Neural Solver for Multiple Object Tracking. In CVPR, 2020.
IQHAT
22. using public detections 		57.1 57.7 45.7 507 (40.8)249 (20.0)32,247 187,937 63.7 91.1 44.7 46.9 50.1 68.8 51.1 73.1 80.5 7.2 1,875 (0.0)2,543 (0.0)7.5
Y. He, X. Wei, X. Hong, W. Ke, Y. Gong. Identity-Quantity Harmonic Multi-Object Tracking. In IEEE Transactions on Image Processing, 2022.
TrTEST
23. using public detections 		57.0 48.8 42.5 499 (40.2)243 (19.6)26,770 190,486 63.2 92.4 38.0 47.9 40.4 79.0 51.8 75.8 82.3 6.0 5,271 (83.4)4,877 (77.2)1.0
center_reid
24. online method using public detections 		56.6 65.0 51.2 668 (53.8)141 (11.4)98,181 121,688 76.5 80.1 51.8 51.0 57.0 72.3 61.6 64.7 79.7 21.9 4,643 (60.7)9,188 (120.1)7.0
ALBOD
25. online method using public detections 		56.5 51.1 43.5 506 (40.7)228 (18.4)36,864 184,582 64.3 90.0 39.7 48.0 42.6 76.5 52.8 73.9 82.0 8.2 3,727 (57.9)4,308 (67.0)1.2
Anonymous submission
LPC_MOT
26. using public detections 		56.3 62.5 49.0 424 (34.1)313 (25.2)11,726 213,056 58.8 96.3 52.4 45.8 54.7 81.3 48.1 78.7 82.3 2.6 1,562 (26.6)1,865 (31.7)0.7
P. Dai, R. Weng, W. Choi, C. Zhang, Z. He, W. Ding. Learning a Proposal Classifier for Multiple Object tracking. In CVPR (Accepted), 2021.
FGRNetIV
27. online method using public detections 		55.4 52.7 42.5 508 (40.9)221 (17.8)38,973 189,715 63.3 89.4 39.3 46.3 46.4 62.2 51.4 72.5 81.8 8.7 2,159 (34.1)3,839 (60.6)1.3
GCNSelfSup
28. online method using public detections 		54.7 52.4 42.7 409 (32.9)302 (24.3)10,731 221,649 57.2 96.5 41.2 44.4 43.6 78.2 46.5 78.5 82.1 2.4 1,829 (0.0)1,857 (0.0)16.9
Sp_Con
29. using public detections 		54.6 53.4 42.5 407 (32.8)317 (25.5)9,486 223,607 56.8 96.9 41.4 43.8 48.2 63.8 46.0 78.4 82.0 2.1 1,674 (0.0)2,455 (0.0)1.4
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.
GNNMatch
30. online method using public detections 		54.5 49.0 40.2 407 (32.8)317 (25.5)9,522 223,611 56.8 96.9 37.0 43.8 44.0 58.7 45.9 78.4 82.0 2.1 2,038 (35.9)2,456 (43.3)0.1
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.
TrackerMOTAIDF1HOTAMTMLFPFNRcllPrcnAssADetAAssReAssPrDetReDetPrLocAFAFID Sw.FragHz
MOT20_TBC
31. using public detections 		54.5 50.1 0.0 415 (33.4)245 (19.7)37,937 195,242 62.3 89.5 0.0 0.0 0.0 0.0 0.0 0.0 100.0 8.5 2,449 (39.3)2,580 (41.4)5.6
Tracking-by-Counting: Using Network Flows on Crowd Density Maps for Tracking Multiple Targets[J]. IEEE Transactions on Image Processing, 2020, 30: 1439-1452.
ArTIST_C
32. online method using public detections 		53.6 51.0 41.6 392 (31.6)349 (28.1)7,765 230,576 55.4 97.4 40.2 43.3 43.6 73.1 45.2 79.3 82.3 1.7 1,531 (27.6)1,959 (35.3)1.0
UnsupTrack
33. online method using public detections 		53.6 50.6 41.7 376 (30.3)311 (25.0)6,439 231,298 55.3 97.8 40.2 43.3 43.2 75.9 45.1 79.8 82.6 1.4 2,178 (39.4)4,335 (78.4)1.3
S. Karthik, A. Prabhu, V. Gandhi. Simple Unsupervised Multi-Object Tracking. In Arxiv, 2020.
Tracktor++v2
34. online method using public detections 		52.6 52.7 42.1 365 (29.4)331 (26.7)6,930 236,680 54.3 97.6 42.0 42.3 45.9 71.6 44.1 79.3 82.4 1.5 1,648 (30.4)4,374 (80.6)1.2
P. Bergmann, T. Meinhardt, L. Leal-Taixé. Tracking without bells and whistles. In ICCV, 2019.
HOMI_Tracker
35. online method using public detections 		51.2 43.0 37.3 423 (34.1)312 (25.1)16,094 232,259 55.1 94.7 32.9 42.5 34.8 78.9 45.0 77.2 82.2 3.6 3,937 (71.4)6,458 (117.2)7.5
SFS
36. online method using public detections 		50.8 41.1 32.7 341 (27.5)251 (20.2)50,139 200,932 61.2 86.3 25.1 43.1 34.5 47.4 48.0 67.8 78.2 11.2 3,503 (57.3)7,617 (124.5)0.1
MTSFS： Online Multi-Object Tracking Based on Salient Feature Selection in Crowded Scenes
SSL_MOT
37. online method using public detections 		48.1 37.5 30.9 421 (33.9)240 (19.3)70,716 186,779 63.9 82.4 21.8 44.3 34.7 34.7 51.4 66.2 80.0 15.8 11,247 (0.0)9,024 (0.0)1.6
BBT
38. using public detections 		46.8 42.2 35.8 312 (25.1)289 (23.3)35,014 236,176 54.4 88.9 32.2 40.2 35.1 70.2 43.8 71.7 80.6 7.8 3,880 (71.4)7,207 (132.6)8.0
MTAP-D-20-01870 Tracking Subjects and Detecting Relationships in Crowded City Videos (under review)
FlowTracker
39. online method using public detections 		46.7 42.4 35.6 345 (27.8)249 (20.0)54,732 217,371 58.0 84.6 31.2 41.3 34.2 68.3 46.4 67.6 79.1 12.2 3,532 (0.0)5,165 (0.0)19.2
H. Nishimura et al., "SDOF-Tracker: Fast and Accurate Multiple Human Tracking by Skipped-Detection and Optical-Flow"
IOU_KMM
40. online method using public detections 		46.5 49.4 40.4 371 (29.9)244 (19.6)57,517 214,777 58.5 84.0 39.4 41.9 41.7 75.2 47.3 67.9 79.9 12.8 4,509 (0.0)7,557 (0.0)30.3
O. Urbann, O. Bredtmann, M. Otten, P. Richter, T. Bauer, D. Zibriczky. Online and Real-Time Tracking in a Surveillance Scenario. In arXiv, 2021.
TrackerMOTAIDF1HOTAMTMLFPFNRcllPrcnAssADetAAssReAssPrDetReDetPrLocAFAFID Sw.FragHz
CT_v0
41. using public detections 		45.1 35.6 33.0 409 (32.9)235 (18.9)69,491 207,927 59.8 81.7 26.3 42.3 27.7 75.4 48.7 66.4 79.8 15.5 6,492 (0.0)6,351 (0.0)317.9
J. Lohn-Jaramillo, L. Ray, R. Granger, E. Bowen. ClusterTracker: An Efficiency-Focused Multiple Object Tracking Method. In , 2022.
GMPHD_Rd20
42. online method using public detections 		44.7 43.5 35.6 293 (23.6)274 (22.1)42,778 236,116 54.4 86.8 32.0 39.9 34.1 72.0 44.0 70.3 80.2 9.6 7,492 (137.8)11,153 (205.1)25.2
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.
Surveily
43. online method using public detections 		44.6 42.5 36.0 393 (31.6)236 (19.0)71,208 211,064 59.2 81.1 32.0 41.2 36.3 62.4 47.5 65.1 78.8 15.9 4,334 (73.2)6,646 (112.2)29.8
SORT20
44. online method using public detections 		42.7 45.1 36.1 208 (16.7)326 (26.2)27,521 264,694 48.8 90.2 35.9 36.7 39.4 66.9 39.5 73.0 81.0 6.1 4,470 (91.5)17,798 (364.4)57.3
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.
OVBT17
45. online method using public detections 		40.0 37.8 30.5 141 (11.4)374 (30.1)23,368 282,949 45.3 90.9 27.4 34.4 31.0 60.6 36.7 73.7 81.1 5.2 4,210 (0.0)10,026 (0.0)0.1
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.
D4C
46. online method using public detections 		7.7 18.6 14.3 9 (0.7)807 (65.0)56,781 417,160 19.4 63.8 14.5 14.5 15.6 57.7 15.2 50.2 66.3 12.7 3,677 (0.0)13,689 (0.0)5.5
ITM
47. online method using public detections 		6.3 10.7 12.5 17 (1.4)1,079 (86.9)1,649 482,442 6.8 95.5 28.9 5.4 30.6 72.0 5.5 77.1 81.2 0.4 556 (0.0)2,777 (0.0)1.8
SequencesFramesTrajectoriesBoxes
444791501765465

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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