MOT17 Results

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

TrackerMOTAIDF1MOTPMTMLFPFNRecallPrecisionFAFID Sw.FragHz
GMPHD_DAL
1. online method using public detections
44.4
±13.9
36.2
±9.2
77.4 350 (14.9)927 (39.4)19,170 283,380 49.8 93.6 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.
GMPHD_N1Tr
2. online method using public detections
42.1
±13.0
33.9
±9.5
77.7 280 (11.9)1,005 (42.7)18,214 297,646 47.2 93.6 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.
HISP_T17
3. online method using public detections
44.6
±0.0
38.8
±0.0
77.2 355 (15.1)913 (38.8)25,478 276,395 51.0 91.9 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.
HISP_DAL17
4. online method using public detections
45.4
±13.9
39.9
±9.5
77.3 349 (14.8)922 (39.2)21,820 277,473 50.8 92.9 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 CoRR, 2019.
MST
5. online method using public detections
39.2
±19.4
37.6
±10.7
75.9 477 (20.3)730 (31.0)89,414 245,273 56.5 78.1 5.0 8,532 (150.9)9,606 (169.9)370.9
FPSN
6. online method using public detections
44.9
±13.8
48.4
±10.6
76.6 388 (16.5)844 (35.8)33,757 269,952 52.2 89.7 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.
IOU17
7. using public detections
45.5
±13.6
39.4
±10.3
76.9 369 (15.7)953 (40.5)19,993 281,643 50.1 93.4 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.
GMPHD_KCF
8. online method using public detections
39.6
±13.0
36.6
±7.6
74.5 208 (8.8)1,019 (43.3)50,903 284,228 49.6 84.6 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.
MTDF17
9. online method using public detections
49.6
±13.7
45.2
±10.5
75.5 444 (18.9)779 (33.1)37,124 241,768 57.2 89.7 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.
MOCL
10. online method using public detections
49.5
±14.0
43.4
±11.2
77.2 487 (20.7)838 (35.6)25,373 254,131 55.0 92.4 1.4 5,164 (94.0)5,787 (105.3)148.0
ECCV-20/4696
TrackerMOTAIDF1MOTPMTMLFPFNRecallPrecisionFAFID Sw.FragHz
Tracker
11. online method using public detections
42.4
±13.9
39.2
±10.5
76.5 272 (11.5)1,045 (44.4)21,614 298,128 47.2 92.5 1.2 5,081 (107.7)6,259 (132.7)66.9
Anonymous submission
SORT17
12. online method using public detections
43.1
±13.3
39.8
±10.3
77.8 295 (12.5)997 (42.3)28,398 287,582 49.0 90.7 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.
GM_PHD
13. online method using public detections
36.4
±14.0
33.9
±9.6
76.2 97 (4.1)1,349 (57.3)23,723 330,767 41.4 90.8 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.
EAMTT
14. online method using public detections
42.6
±13.3
41.8
±9.7
76.0 300 (12.7)1,006 (42.7)30,711 288,474 48.9 90.0 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.
MASS
15. online method using public detections
46.9
±14.2
46.0
±10.7
76.1 399 (16.9)856 (36.3)25,733 269,116 52.3 92.0 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.
PHD_GM
16. online method using public detections
48.8
±13.0
43.2
±9.3
76.7 449 (19.1)830 (35.2)26,260 257,971 54.3 92.1 1.5 4,407 (81.2)6,448 (118.8)22.3
R. Sanchez-Matilla, A. Cavallaro. A predictor of moving objects for First-Person vision. In Proceedings of IEEE International Conference Image Processing, 2019.
DASOT17
17. online method using public detections
49.5
±14.0
51.8
±10.6
76.9 481 (20.4)814 (34.6)33,640 247,370 56.2 90.4 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.
MSRRT
18. online method using public detections
57.1
±11.1
49.3
±8.0
77.9 590 (25.1)790 (33.5)26,180 211,947 62.4 93.1 1.5 4,114 (65.9)4,595 (73.6)5.9
Anonymous submission
EDA_GNN
19. online method using public detections
45.5
±13.8
40.5
±10.2
76.3 368 (15.6)955 (40.6)25,685 277,663 50.8 91.8 1.4 4,091 (80.5)5,579 (109.8)39.3
Paper ID 2713
PHD_GSDL17
20. online method using public detections
48.0
±13.6
49.6
±9.4
77.2 402 (17.1)838 (35.6)23,199 265,954 52.9 92.8 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.
TrackerMOTAIDF1MOTPMTMLFPFNRecallPrecisionFAFID Sw.FragHz
GMPHD_Rd17
21. online method using public detections
46.8
±14.7
54.1
±9.2
76.4 464 (19.7)784 (33.3)38,452 257,678 54.3 88.9 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 a CNN-based Re-identification. In , 2019.
GMPHD_SHA
22. online method using public detections
43.7
±0.0
39.2
±0.0
76.5 276 (11.7)1,012 (43.0)25,935 287,758 49.0 91.4 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.
MADA_NET
23. online method using public detections
57.6
±13.7
53.2
±11.0
78.5 523 (22.2)813 (34.5)10,475 224,948 60.1 97.0 0.6 3,657 (60.8)4,262 (70.9)1,233.1
Anonymous submission
OTCD_1
24. online method using public detections
48.6
±13.6
47.9
±11.1
76.9 382 (16.2)970 (41.2)18,499 268,204 52.5 94.1 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.
GMPHDOGM17
25. online method using public detections
49.9
±13.3
47.1
±8.3
77.0 464 (19.7)895 (38.0)24,024 255,277 54.8 92.8 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.
FAMNet
26. online method using public detections
52.0
±12.1
48.7
±9.9
76.5 450 (19.1)787 (33.4)14,138 253,616 55.1 95.6 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.
JBNOT
27. using public detections
52.6
±12.3
50.8
±9.6
77.1 465 (19.7)844 (35.8)31,572 232,659 58.8 91.3 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.
ISDH_HDAv2
28. online method using public detections
54.5
±14.5
65.9
±10.8
75.2 622 (26.4)755 (32.1)46,693 207,093 63.3 88.4 2.6 3,010 (47.6)6,000 (94.8)3.6
MM-008988/ IEEE Transactions on Multimedia
MOT_TBC
29. using public detections
53.9
±15.6
50.0
±10.2
76.8 476 (20.2)864 (36.7)24,584 232,670 58.8 93.1 1.4 2,945 (50.1)4,612 (78.5)6.7
Anonymous submission
DGCT
30. using public detections
54.5
±13.0
51.3
±10.7
79.0 495 (21.0)834 (35.4)10,471 243,143 56.9 96.8 0.6 2,865 (50.3)4,889 (85.9)7.0
CJY, HYW, KHW @ HRI-SH
TrackerMOTAIDF1MOTPMTMLFPFNRecallPrecisionFAFID Sw.FragHz
DAIST
31. online method using public detections
52.0
±12.5
53.9
±7.5
76.4 476 (20.2)787 (33.4)31,275 237,004 58.0 91.3 1.8 2,817 (48.6)5,875 (101.3)6.9
Anonymous submission
DAIST_
32. online method using public detections
52.1
±12.5
53.8
±7.5
76.5 463 (19.7)792 (33.6)29,931 237,913 57.8 91.6 1.7 2,689 (46.5)5,600 (96.8)6.9
Anonymous submission
GNN_tracktor
33. online method using public detections
54.4
±12.9
54.1
±11.3
77.4 420 (17.8)880 (37.4)12,655 241,868 57.1 96.2 0.7 2,660 (46.6)3,991 (69.9)1.7
Anonymous submission
FWT
34. using public detections
51.3
±13.2
47.6
±11.5
77.0 505 (21.4)830 (35.2)24,101 247,921 56.1 92.9 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.
msot
35. online method using public detections
59.2
±11.1
51.8
±9.2
78.0 630 (26.8)669 (28.4)18,968 208,569 63.0 94.9 1.1 2,648 (42.0)3,399 (53.9)0.9
Anonymous submission
AFN17
36. using public detections
51.5
±12.7
46.9
±8.3
77.6 485 (20.6)836 (35.5)22,391 248,420 56.0 93.4 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.
ISE_MOT17R
37. online method using public detections
60.1
±11.4
56.4
±9.2
78.2 672 (28.5)661 (28.1)23,168 199,483 64.6 94.0 1.3 2,556 (39.5)3,182 (49.2)7.2
MIFT
TrajTrack
38. online method using public detections
56.0
±12.9
57.2
±10.5
78.6 532 (22.6)826 (35.1)14,378 231,212 59.0 95.9 0.8 2,546 (43.1)3,452 (58.5)1.4
Anonymous submission
TLO17
39. online method using public detections
52.6
±12.9
51.3
±9.7
76.6 460 (19.5)899 (38.2)20,089 244,930 56.6 94.1 1.1 2,530 (44.7)4,170 (73.7)25.2
Anonymous submission
CRF_TRA
40. using public detections
53.1
±12.1
53.7
±9.3
76.1 571 (24.2)724 (30.7)27,194 234,991 58.4 92.4 1.5 2,518 (43.2)4,918 (84.3)1.4
Jun xiang, Chao Ma, Guohan Xu, Jianhua Hou, End-to-End Learning Deep CRF models for Multi-Object Tracking. In IEEE Transactions on Circuits and Systems for Video Technology, 2020
TrackerMOTAIDF1MOTPMTMLFPFNRecallPrecisionFAFID Sw.FragHz
MOTDT17
41. online method using public detections
50.9
±11.7
52.7
±7.5
76.6 413 (17.5)841 (35.7)24,069 250,768 55.6 92.9 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.
TLO_MHT
42. online method using public detections
53.3
±12.9
53.3
±9.5
76.5 471 (20.0)912 (38.7)22,161 238,959 57.6 93.6 1.2 2,434 (42.2)4,089 (70.9)2.0
Anonymous submission
STRN_MOT17
43. online method using public detections
50.9
±11.6
56.0
±9.0
75.6 446 (18.9)797 (33.8)25,295 249,365 55.8 92.6 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.
Seq2Seq
44. using public detections
52.7
±12.1
49.4
±9.4
77.0 417 (17.7)900 (38.2)10,819 253,890 55.0 96.6 0.6 2,396 (43.6)3,374 (61.3)2.6
Anonymous submission
track_bnw
45. online method using public detections
56.7
±13.4
52.1
±10.5
78.8 543 (23.1)813 (34.5)8,895 233,206 58.7 97.4 0.5 2,351 (40.1)3,155 (53.8)0.7
Anonymous submission
MHT_DAM
46. using public detections
50.7
±13.7
47.2
±9.6
77.5 491 (20.8)869 (36.9)22,875 252,889 55.2 93.2 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.
eTC17
47. using public detections
51.9
±12.9
58.1
±8.7
76.3 544 (23.1)836 (35.5)36,164 232,783 58.7 90.2 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.
LM_NN
48. using public detections
45.1
±13.3
43.2
±10.3
78.9 348 (14.8)1,088 (46.2)10,834 296,451 47.5 96.1 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.
NOTA
49. using public detections
51.3
±11.7
54.5
±7.6
76.7 403 (17.1)833 (35.4)20,148 252,531 55.2 93.9 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.
EDMT17
50. using public detections
50.0
±13.9
51.3
±10.2
77.3 509 (21.6)855 (36.3)32,279 247,297 56.2 90.8 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.
TrackerMOTAIDF1MOTPMTMLFPFNRecallPrecisionFAFID Sw.FragHz
AM_ADM17
51. online method using public detections
48.1
±13.5
52.1
±10.7
76.7 316 (13.4)934 (39.7)25,061 265,495 52.9 92.3 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.
DMAN
52. online method using public detections
48.2
±12.3
55.7
±9.4
75.7 454 (19.3)902 (38.3)26,218 263,608 53.3 92.0 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.
DEEP_TAMA
53. online method using public detections
50.3
±13.3
53.5
±10.1
76.7 453 (19.2)883 (37.5)25,479 252,996 55.2 92.4 1.4 2,192 (39.7)3,978 (72.1)1.5
Y. Yoon, D. Kim, K. Yoon, Y. Song, M. Jeon. Online Multiple Pedestrian Tracking using Deep Temporal Appearance Matching Association. In arXiv:1907.00831, 2019.
LSST17O
54. online method using public detections
52.7
±13.0
57.9
±9.3
76.2 421 (17.9)863 (36.6)22,512 241,936 57.1 93.5 1.3 2,167 (37.9)7,443 (130.3)1.8
Multi-Object Tracking with Multiple Cues and Switcher-Aware Classification
DAM_MOT
55. online method using public detections
47.0
±0.0
48.7
±0.0
76.9 397 (16.9)897 (38.1)28,933 267,896 52.5 91.1 1.6 2,140 (40.7)2,756 (52.5)18.7
Multi Object Tracking using Deep Structural Cost Minimization in Data Association
YOONKJ17
56. online method using public detections
51.4
±12.9
54.0
±9.5
77.0 500 (21.2)878 (37.3)29,051 243,202 56.9 91.7 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.
Tracktor++
57. online method using public detections
53.5
±13.6
52.3
±10.6
78.0 459 (19.5)861 (36.6)12,201 248,047 56.0 96.3 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.
MHT_bLSTM
58. using public detections
47.5
±12.6
51.9
±9.8
77.5 429 (18.2)981 (41.7)25,981 268,042 52.5 91.9 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.
TLO_bnw
59. using public detections
56.8
±13.6
56.0
±9.1
77.7 518 (22.0)872 (37.0)18,149 223,859 60.3 94.9 1.0 2,004 (33.2)3,567 (59.1)7.4
Anonymous submission
ALBOD
60. online method using public detections
56.9
±12.1
58.7
±8.4
77.9 693 (29.4)633 (26.9)49,568 191,764 66.0 88.3 2.8 2,001 (30.3)3,163 (47.9)4.9
Anonymous submission
TrackerMOTAIDF1MOTPMTMLFPFNRecallPrecisionFAFID Sw.FragHz
Tracktor++v2
61. online method using public detections
56.3
±13.3
55.1
±10.8
78.8 498 (21.1)831 (35.3)8,866 235,449 58.3 97.4 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.
TrctrD17
62. online method using public detections
53.7
±13.3
53.8
±10.6
77.2 458 (19.4)861 (36.6)11,731 247,447 56.1 96.4 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.
HDTR
63. using public detections
54.1
±11.5
48.4
±8.0
80.2 549 (23.3)819 (34.8)18,002 238,818 57.7 94.8 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.
UNS20
64. online method using public detections
51.3
±12.3
51.4
±10.0
76.3 432 (18.3)939 (39.9)13,599 259,389 54.0 95.7 0.8 1,872 (34.6)3,303 (61.1)12.2
Anonymous submission
HAM_SADF17
65. online method using public detections
48.3
±13.2
51.1
±10.3
77.2 402 (17.1)981 (41.7)20,967 269,038 52.3 93.4 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.
UnsupTrack
66. online method using public detections
61.7
±16.0
58.1
±11.1
78.3 640 (27.2)762 (32.4)16,872 197,632 65.0 95.6 1.0 1,864 (28.7)4,213 (64.8)2.0
Simple Unsupervised Multi-Object Tracking (Under Review)
eHAF17
67. using public detections
51.8
±13.2
54.7
±9.1
77.0 551 (23.4)893 (37.9)33,212 236,772 58.0 90.8 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.
jCC
68. using public detections
51.2
±14.5
54.5
±10.9
75.9 493 (20.9)872 (37.0)25,937 247,822 56.1 92.4 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.
VAN
69. online method using public detections
57.4
±12.8
57.9
±10.3
78.7 619 (26.3)794 (33.7)14,316 224,064 60.3 96.0 0.8 1,788 (29.7)2,586 (42.9)6.8
Anonymous submission
TARCA
70. online method using public detections
55.9
±13.3
58.1
±9.1
77.5 571 (24.2)846 (35.9)20,141 227,151 59.7 94.4 1.1 1,784 (29.9)3,741 (62.6)6.9
Anonymous submission
TrackerMOTAIDF1MOTPMTMLFPFNRecallPrecisionFAFID Sw.FragHz
ENFT17
71. using public detections
52.8
±13.1
57.1
±8.5
77.4 543 (23.1)867 (36.8)26,754 237,909 57.8 92.4 1.5 1,667 (28.8)2,557 (44.2)0.5
BUAA
SAS_MOT17
72. using public detections
44.2
±0.0
57.2
±0.0
76.4 379 (16.1)1,044 (44.3)29,473 283,611 49.7 90.5 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.
TLMHT
73. using public detections
50.6
±12.5
56.5
±7.8
77.6 415 (17.6)1,022 (43.4)22,213 255,030 54.8 93.3 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.
GMOT
74. using public detections
55.4
±12.2
57.9
±7.8
76.7 535 (22.7)817 (34.7)20,608 229,511 59.3 94.2 1.2 1,403 (23.7)2,765 (46.6)5.9
LXD, KHW @ HRI-SH
LSST17
75. using public detections
54.7
±12.9
62.3
±9.5
75.9 480 (20.4)944 (40.1)26,091 228,434 59.5 92.8 1.5 1,243 (20.9)3,726 (62.6)1.5
Multi-Object Tracking with Multiple Cues and Switcher-Aware Classification
CMT
76. using public detections
51.8
±12.7
60.7
±8.6
77.3 462 (19.6)1,009 (42.8)29,528 240,960 57.3 91.6 1.7 1,217 (21.2)2,008 (35.0)6.5
#Submission: TIP-21190-2019
Lif_T
77. using public detections
60.5
±13.0
65.6
±8.6
78.3 637 (27.0)791 (33.6)14,966 206,619 63.4 96.0 0.8 1,189 (18.8)3,476 (54.8)0.5
Anonymous submission
MPNTrack
78. using public detections
58.8
±12.4
61.7
±8.7
78.6 679 (28.8)788 (33.5)17,413 213,594 62.1 95.3 1.0 1,185 (19.1)2,265 (36.4)6.5
G. Brasó, L. Leal-Taixé. Learning a Neural Solver for Multiple Object Tracking. In CVPR, 2020.
TT17
79. using public detections
54.9
±11.6
63.1
±8.6
77.2 575 (24.4)897 (38.1)20,236 233,295 58.7 94.2 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.
SequencesFramesTrajectoriesBoxes
21177572355564228

Difficulty Analysis

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

MOT17-03-SDP

MOT17-03-SDP

(73.6 MOTA)

MOT17-03-FRCNN

MOT17-03-FRCNN

(61.8 MOTA)

MOT17-03-DPM

MOT17-03-DPM

(55.4 MOTA)

...

...

MOT17-14-DPM

MOT17-14-DPM

(25.5 MOTA)

MOT17-14-FRCNN

MOT17-14-FRCNN

(24.3 MOTA)


Evaluation Measures

Lower is better. Higher is better.
Measure Better Perfect Description
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. 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 public detections This method used the provided detection set as input.
new This entry has been submitted or updated less than a week ago.
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] 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.