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
±11.6
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
TMOH
2. online method using public detections
60.1
±19.9
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.
xumot
3. using public detections
59.7
±23.6
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
TransCtr
4. online method using public detections
59.4
±21.4
48.0 42.0 592 (47.7)174 (14.0)57,384 148,044 71.4 86.6 34.5 51.7 43.2 57.5 58.6 71.1 81.2 12.8 4,896 (0.0)9,234 (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.
mfi_tst
5. online method using public detections
59.3
±20.4
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
6. online method using public detections
59.1
±20.7
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.
ApLift
7. using public detections
58.9
±20.3
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
MPNTrack
8. using public detections
57.6
±19.7
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. Brasó, L. Leal-Taixé. Learning a Neural Solver for Multiple Object Tracking. In CVPR, 2020.
TrTEST
9. using public detections
57.0
±20.4
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
10. online method using public detections
56.6
±18.3
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
TrackerMOTAIDF1HOTAMTMLFPFNRcllPrcnAssADetAAssReAssPrDetReDetPrLocAFAFID Sw.FragHz
ALBOD
11. online method using public detections
56.5
±20.8
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
12. using public detections
56.3
±18.9
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
13. online method using public detections
55.4
±17.6
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
D4C
14. online method using public detections
54.8
±15.1
64.4 51.5 713 (57.4)109 (8.8)121,939 106,271 79.5 77.1 51.6 51.7 57.6 70.7 64.8 62.9 80.1 27.2 5,792 (0.0)10,119 (0.0)5.5
Sp_Con
15. using public detections
54.6
±20.0
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 arXiv preprint arXiv:2105.02426, 2021.
GNNMatch
16. online method using public detections
54.5
±20.0
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.
MOT20_TBC
17. using public detections
54.5
±17.2
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
18. online method using public detections
53.6
±19.4
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
19. online method using public detections
53.6
±19.0
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
20. online method using public detections
52.6
±19.9
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.
TrackerMOTAIDF1HOTAMTMLFPFNRcllPrcnAssADetAAssReAssPrDetReDetPrLocAFAFID Sw.FragHz
HOMI_Tracker
21. online method using public detections
51.2
±22.6
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
22. online method using public detections
50.8
±17.4
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
ITM
23. online method using public detections
50.6
±22.3
48.6 39.6 397 (32.0)320 (25.8)19,495 233,940 54.8 93.6 38.1 41.3 43.0 67.5 44.1 75.3 81.2 4.4 2,431 (44.4)4,533 (82.7)1.8
Pivot Correlation Network with Individualized Tubelets for Efficient Multi-object Tracking
BBT
24. using public detections
46.8
±21.3
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
25. online method using public detections
46.7
±22.0
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
SDOF-Tracker: Fast and Accurate Multiple Human Tracking by Skipped-Detection and Optical-Flow.
IOU_KMM
26. online method using public detections
46.5
±23.8
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.
GMPHD_Rd20
27. online method using public detections
44.7
±20.7
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
Surveily
28. online method using public detections
44.6
±24.3
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
29. online method using public detections
42.7
±18.6
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.
SequencesFramesTrajectoriesBoxes
444791501765465

Difficulty Analysis

Sequence difficulty (from easiest to hardest, measured by average MOTA)

MOT20-04

MOT20-04

(73.0 MOTA)

MOT20-07

MOT20-07

(57.6 MOTA)

MOT20-06

MOT20-06

(33.1 MOTA)

MOT20-08

MOT20-08

(23.3 MOTA)

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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