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

Tracker MOTAIDF1MTMLFPFNID Sw.FragHzDetector
GNNT
1. online method using public detections
-523.7
±1,341.9
9.033.6% 27.2% 3,318,414185,81915,019 (223.9)4,763 (71.0)7.6Public
Anonymous submission
MOT_BJ
2. online method using public detections
-7.3
±23.5
1.40.0% 99.1% 52,007548,5314,824 (1,734.0)8,621 (3,098.8)0.0Public
Anonymous submission
JOINT_TRAC
3. using public detections
29.4
±17.6
34.512.6% 42.3% 132,192260,8085,397 (100.4)10,704 (199.0)66.9Public
Anonymous submission
YoloSort
4. online method using public detections
29.5
±24.1
41.715.0% 36.4% 154,747238,2414,888 (84.6)4,952 (85.7)14.4Public
Anonymous submission
GM_PHD
5. online method using public detections
36.4
±14.1
33.94.1% 57.3% 23,723330,7674,607 (111.3)11,317 (273.5)38.4Public
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.
baitrack
6. using public detections
37.6
±19.4
20.321.0% 30.9% 99,085244,0018,808 (155.2)6,708 (118.2)6.4Public
Anonymous submission
GLMBS3
7. using public detections
38.0
±13.7
32.39.3% 52.8% 38,874304,0166,963 (151.0)3,927 (85.2)4.9Public
Anonymous submission
GoturnM17
8. online method using public detections
38.3
±9.0
25.79.4% 47.1% 55,381282,67010,328 (207.0)9,849 (197.4)11.8Public
Anonymous submission
CTRACKER
9. online method using public detections
39.4
±13.5
26.113.4% 42.5% 16,249307,90017,592 (387.2)14,508 (319.3)66.9Public
Anonymous submission
GMPHD_KCF
10. online method using public detections
39.6
±13.5
36.68.8% 43.3% 50,903284,2285,811 (117.1)7,414 (149.4)3.3Public
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.
Tracker MOTAIDF1MTMLFPFNID Sw.FragHzDetector
XYHv2
11. online method using public detections
39.9
±12.4
23.89.9% 41.8% 29,713296,70412,900 (272.1)12,911 (272.3)7.8Public
Anonymous submission
98K
12. using public detections
40.8
±17.2
37.015.6% 38.1% 32,312298,1743,514 (74.5)4,991 (105.8)17.7Public
Anonymous submission
SRPN17
13. online method using public detections
40.8
±15.2
40.512.0% 48.0% 9,293321,8112,801 (65.2)7,120 (165.7)4.1Public
Anonymous submission
TM_track
14. online method using public detections
41.1
±14.9
32.813.2% 41.3% 27,606287,51117,408 (355.0)15,197 (309.9)2.5Public
Anonymous submission
CGHA_MOT
15. online method using public detections
41.2
±14.1
44.08.3% 45.9% 25,462299,1127,294 (155.2)18,655 (397.0)11.4Public
Anonymous submission
c3d_Track
16. online method using public detections
41.5
±13.7
40.210.7% 48.5% 33,332292,9313,890 (80.9)11,454 (238.2)22.2Public
Anonymous submission
cascademot
17. online method using public detections
41.8
±16.0
34.215.2% 38.7% 27,816288,76811,535 (236.3)14,800 (303.2)17.8Public
Anonymous submission
GNNMOT
18. online method using public detections
42.0
±12.8
29.312.0% 50.0% 23,294299,6944,377 (93.4)3,847 (82.1)177.6Public
Anonymous submission
GMPHD_N1Tr
19. online method using public detections
42.1
±13.2
33.911.9% 42.7% 18,214297,64610,698 (226.4)10,864 (229.9)9.9Public
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.
GM_PHD
20. online method using public detections
42.1
±13.2
33.911.9% 42.7% 18,214297,64610,698 (226.4)10,864 (229.9)9.9Public
Anonymous submission
Tracker MOTAIDF1MTMLFPFNID Sw.FragHzDetector
CASC_MOT
21. online method using public detections
42.3
±12.8
46.89.1% 44.1% 21,035300,7973,616 (77.4)16,656 (356.7)11.4Public
Anonymous submission
ms_dh
22. online method using public detections
42.6
±14.6
40.113.6% 40.0% 31,878284,5287,446 (150.2)14,736 (297.3)4.0Public
Anonymous submission
EAMTT
23. online method using public detections
42.6
±13.3
41.812.7% 42.7% 30,711288,4744,488 (91.8)5,720 (117.0)12.0Public
R. Sanchez-Matilla, F. Poiesi, A. Cavallaro. Online Multi-target Tracking with Strong and Weak Detections. In Computer Vision -- ECCV 2016 Workshops, 2016.
D_SST_V1
24. online method using public detections
42.7
±13.9
46.111.8% 44.4% 18,861298,9895,531 (117.7)13,775 (293.0)2.3Public
Anonymous submission
DAN__test
25. using public detections
43.0
±14.7
43.313.5% 40.0% 30,367283,5337,576 (152.3)14,990 (301.3)1.8Public
Anonymous submission
SORT17
26. online method using public detections
43.1
±13.3
39.812.5% 42.3% 28,398287,5824,852 (99.0)7,127 (145.4)143.3Public
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.
TPbase17
27. online method using public detections
43.3
±15.0
48.216.2% 36.6% 49,992265,8154,194 (79.3)12,103 (228.8)22.2Public
Anonymous submission
ZM
28. online method using public detections
43.5
±13.9
32.614.5% 39.9% 25,083284,4059,197 (185.4)8,849 (178.4)14.4Public
Anonymous submission
MOTbyReID
29. online method using public detections
43.6
±13.7
37.117.6% 36.3% 35,725270,03612,347 (236.8)11,408 (218.8)2.5Public
Anonymous submission
GMPHD_SHA
30. online method using public detections
43.7
±12.5
39.211.7% 43.0% 25,935287,7583,838 (78.3)5,056 (103.2)9.2Public
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.
Tracker MOTAIDF1MTMLFPFNID Sw.FragHzDetector
Umot
31. online method using public detections
43.9
±13.8
37.815.2% 38.9% 28,596278,6219,363 (185.0)11,371 (224.6)19.7Public
Anonymous submission
GM_PHD_D
32. online method using public detections
44.0
±13.8
34.214.8% 39.4% 19,135283,53013,556 (272.5)13,821 (277.8)9.9Public
Anonymous submission
SAS_MOT17
33. using public detections
44.2
±12.2
57.216.1% 44.3% 29,473283,6111,529 (30.7)2,644 (53.2)4.8Public
A. Maksai, P. Fua. Eliminating Exposure Bias and Metric Mismatch in Multiple Object Tracking. In CVPR, 2019.
GOTURN_3B
34. online method using public detections
44.3
±13.7
38.513.0% 43.2% 30,302279,1444,861 (96.2)5,277 (104.4)48.6Public
Anonymous submission
GMPHD_DAL
35. online method using public detections
44.4
±13.9
36.214.9% 39.4% 19,170283,38011,137 (223.7)13,900 (279.3)3.4Public
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.
HISP_T17
36. online method using public detections
44.6
±14.2
38.815.1% 38.8% 25,478276,39510,617 (208.1)7,487 (146.8)4.7Public
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.
FPSN
37. online method using public detections
44.9
±13.9
48.416.5% 35.8% 33,757269,9527,136 (136.8)14,491 (277.8)10.1Public
S. Lee, E. Kim. Multiple Object Tracking via Feature Pyramid Siamese Networks. In IEEE ACCESS, 2018.
DSA_MOT17
38. online method using public detections
45.0
±12.6
43.615.8% 39.2% 21,442286,4822,491 (50.6)3,824 (77.7)9.9Public
Anonymous submission
dcor
39. online method using public detections
45.0
±14.2
34.015.4% 38.2% 30,231275,2654,801 (93.7)8,498 (165.9)44.4Public
Anonymous submission
GF
40. online method using public detections
45.0
±13.9
39.115.0% 39.0% 22,387277,33510,397 (204.5)7,421 (145.9)9.9Public
Anonymous submission
Tracker MOTAIDF1MTMLFPFNID Sw.FragHzDetector
zxbtk17
41. online method using public detections
45.1
±14.7
40.017.7% 31.8% 33,186273,5313,303 (64.1)8,148 (158.1)8.3Public
Anonymous submission
LM_NN
42. using public detections
45.1
±13.3
43.214.8% 46.2% 10,834296,4512,286 (48.2)2,463 (51.9)0.9Public
M. Babaee, Z. Li, G. Rigoll. A Dual CNN--RNN for Multiple People Tracking. In Neurocomputing, 2019.
HISP_DAL17
43. online method using public detections
45.4
±13.9
39.914.8% 39.2% 21,820277,4738,727 (171.7)7,147 (140.6)3.2Public
N. Baisa. Robust Online Multi-target Visual Tracking using a HISP Filter with Discriminative Deep Appearance Learning. In CoRR, 2019.
DCORV2
44. online method using public detections
45.5
±13.9
36.114.6% 40.4% 21,161282,9013,592 (72.0)7,696 (154.4)35.5Public
Anonymous submission
IOU17
45. using public detections
45.5
±13.6
39.415.7% 40.5% 19,993281,6435,988 (119.6)7,404 (147.8)1,522.9Public
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.
EDA_GNN
46. online method using public detections
45.5
±13.8
40.515.6% 40.6% 25,685277,6634,091 (80.5)5,579 (109.8)39.3Public
Paper ID 2713
OLGT_new
47. online method using public detections
45.7
±22.8
49.410.8% 75.5% 6,915298,2881,418 (30.1)3,641 (77.2)6.1Public
Anonymous submission
DS_TW_F
48. online method using public detections
45.7
±27.0
50.910.8% 75.4% 6,528298,3681,329 (28.2)3,180 (67.5)66.9Public
Anonymous submission
PHD_LMP
49. online method using public detections
45.9
±13.1
42.515.5% 37.9% 27,946272,1964,977 (96.2)6,985 (135.0)29.4Public
Anonymous submission
TTracker
50. online method using public detections
46.2
±14.0
44.021.0% 35.6% 44,854254,4384,258 (77.6)6,307 (114.9)29.6Public
Anonymous submission
Tracker MOTAIDF1MTMLFPFNID Sw.FragHzDetector
AEb_O
51. online method using public detections
46.4
±13.9
44.916.5% 41.4% 17,030283,2652,266 (45.5)5,053 (101.5)1.8Public
Anonymous submission
UNS20
52. online method using public detections
46.5
±13.6
47.716.3% 43.1% 19,283280,7881,967 (39.2)3,103 (61.8)12.2Public
Anonymous submission
MHT_ReID7
53. using public detections
46.5
±13.7
46.918.8% 40.3% 22,203276,3743,386 (66.4)8,521 (167.0)1.6Public
Anonymous submission
KVIOU
54. online method using public detections
46.6
±14.2
44.017.3% 38.1% 34,838262,0084,379 (81.8)7,844 (146.4)29.6Public
Anonymous submission
SNM17
55. online method using public detections
46.8
±13.8
43.416.2% 37.1% 25,104271,0424,213 (81.1)9,891 (190.3)0.8Public
Anonymous submission
GMPHD_Rd17
56. online method using public detections
46.8
±14.7
54.119.7% 33.3% 38,452257,6783,865 (71.1)8,097 (149.0)30.8Public
N. Baisa. Occlusion-robust Online Multi-object Visual Tracking using a GM-PHD Filter with a CNN-based Re-identification. In , 2019.
Lab031
57. using public detections
46.9
±16.2
48.117.7% 36.1% 31,634263,9383,795 (71.3)10,498 (197.3)9.4Public
Anonymous submission
MASS
58. online method using public detections
46.9
±14.1
46.016.9% 36.3% 25,733269,1164,478 (85.6)11,994 (229.3)17.1Public
H. Karunasekera, H. Wang, H. Zhang. Multiple Object Tracking With Attention to Appearance, Structure, Motion and Size. In IEEE Access, 2019.
DAM_MOT
59. online method using public detections
47.0
±12.6
48.716.9% 38.1% 28,933267,8962,140 (40.7)2,756 (52.5)18.7Public
Multi Object Tracking using Deep Structural Cost Minimization in Data Association
QiMOT
60. online method using public detections
47.2
±13.1
40.815.5% 39.9% 18,907274,8284,320 (84.2)5,917 (115.4)1.8Public
Anonymous submission
Tracker MOTAIDF1MTMLFPFNID Sw.FragHzDetector
MOT_HY
61. using public detections
47.3
±121.2
49.417.2% 33.8% 46,875246,0614,231 (75.0)8,188 (145.2)2.0Public
Anonymous submission
JDT
62. online method using public detections
47.4
±12.2
50.116.8% 37.2% 26,910267,3312,760 (52.5)6,211 (118.0)35.1Public
Anonymous submission
E2EM
63. online method using public detections
47.5
±14.5
48.816.5% 37.5% 20,655272,1873,632 (70.2)12,712 (245.6)29.6Public
Anonymous submission
TCT
64. online method using public detections
47.5
±27.1
49.323.2% 28.8% 52,209238,7165,541 (96.0)7,368 (127.7)14.1Public
Anonymous submission
MHT_bLSTM
65. using public detections
47.5
±12.6
51.918.2% 41.7% 25,981268,0422,069 (39.4)3,124 (59.5)1.9Public
C. Kim, F. Li, J. Rehg. Multi-object Tracking with Neural Gating Using Bilinear LSTM. In ECCV, 2018.
Alex
66. online method using public detections
47.6
±14.2
49.813.2% 41.4% 16,028277,1102,731 (53.7)8,481 (166.7)0.2Public
Anonymous submission
LT17
67. online method using public detections
47.7
±16.6
45.217.3% 36.0% 27,856263,0624,042 (75.7)9,183 (172.0)7.2Public
Anonymous submission
SRPN
68. online method using public detections
47.8
±13.2
41.417.0% 41.7% 38,279251,9894,325 (78.2)5,355 (96.8)11.8Public
Anonymous submission
cnt_klt
69. using public detections
48.0
±11.8
57.819.0% 31.6% 63,207228,7831,215 (20.4)4,134 (69.5)59.2Public
Anonymous submission
PHD_GSDL17
70. online method using public detections
48.0
±13.6
49.617.1% 35.6% 23,199265,9543,998 (75.6)8,886 (168.1)6.7Public
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.
Tracker MOTAIDF1MTMLFPFNID Sw.FragHzDetector
AEb
71. using public detections
48.1
±13.6
46.017.7% 39.5% 16,839273,8192,350 (45.7)5,275 (102.5)66.9Public
AM_ADM17
72. online method using public detections
48.1
±13.8
52.113.4% 39.7% 25,061265,4952,214 (41.8)5,027 (94.9)5.7Public
S. Lee, M. Kim, S. Bae, Learning Discriminative Appearance Models for Online Multi-Object Tracking with Appearance Discriminability Measures, In IEEE Access, 2018.
DeepMOTRPN
73. online method using public detections
48.1
±14.5
43.017.6% 38.6% 26,490262,5783,696 (69.1)5,353 (100.1)4.9Public
Anonymous submission
2MPT
74. using public detections
48.1
±14.2
52.917.4% 39.6% 30,650260,1331,860 (34.5)2,784 (51.7)2.7Public
Anonymous submission
RegTL
75. using public detections
48.1
±13.7
41.618.1% 39.8% 20,850268,3633,386 (64.6)4,524 (86.3)17.8Public
Anonymous submission
DMAN
76. online method using public detections
48.2
±12.3
55.719.3% 38.3% 26,218263,6082,194 (41.2)5,378 (100.9)0.3Public
J. Zhu, H. Yang, N. Liu, M. Kim, W. Zhang, M. Yang. Online Multi-Object Tracking with Dual Matching Attention Networks. In ECCV, 2018.
HAM_SADF17
77. online method using public detections
48.3
±13.2
51.117.1% 41.7% 20,967269,0381,871 (35.8)3,020 (57.7)5.0Public
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.
SOTD_MC
78. online method using public detections
48.4
±15.0
45.519.4% 35.9% 33,525255,0912,531 (46.2)4,944 (90.2)67.0Public
Anonymous submission
DualAtte
79. online method using public detections
48.4
±14.5
43.717.6% 39.0% 24,915262,6543,423 (64.0)5,192 (97.1)0.3Public
Anonymous submission
RFTracking
80. online method using public detections
48.5
±14.8
44.917.7% 38.6% 25,739261,7103,089 (57.6)4,813 (89.8)66.9Public
Anonymous submission
Tracker MOTAIDF1MTMLFPFNID Sw.FragHzDetector
wangs
81. online method using public detections
48.5
±14.2
45.018.3% 38.8% 25,428261,7553,233 (60.3)4,941 (92.2)66.9Public
Anonymous submission
SiaIOU
82. using public detections
48.5
±16.7
48.518.9% 38.8% 26,867260,2783,152 (58.5)4,391 (81.5)8.3Public
Anonymous submission
OTCD_1
83. online method using public detections
48.6
±13.7
47.916.2% 41.2% 18,499268,2043,502 (66.7)5,588 (106.5)15.5Public
Q. Liu, B. Liu, Y. Wu, W. Li, N. Yu. Real-Time Online Multi-Object Tracking in Compressed Domain. In IEEE Access, 2019.
PHD_GM
84. online method using public detections
48.8
±13.4
43.219.1% 35.2% 26,260257,9714,407 (81.2)6,448 (118.8)22.3Public
R. Sanchez-Matilla, A. Cavallaro. A predictor of moving objects for First-Person vision. In Proceedings of IEEE International Conference Image Processing, 2019.
MOCL
85. online method using public detections new
49.5
±14.0
43.420.7% 35.6% 25,373254,1315,164 (94.0)5,787 (105.3)148.0Public
ECCV-20/4696
MTDF17
86. online method using public detections
49.6
±13.9
45.218.9% 33.1% 37,124241,7685,567 (97.4)9,260 (162.0)1.2Public
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.
OST
87. using public detections
49.7
±14.0
50.417.0% 36.7% 21,811258,6493,077 (56.8)4,339 (80.1)1.7Public
Anonymous submission
lbc_mot
88. using public detections
49.8
±14.3
52.320.3% 36.2% 20,963259,5382,638 (48.9)5,303 (98.2)66.9Public
Anonymous submission
GMPHDOGM17
89. online method using public detections
49.9
±13.6
47.119.7% 38.0% 24,024255,2773,125 (57.1)3,540 (64.6)30.7Public
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.
SFS
90. online method using public detections
50.0
±12.2
51.819.2% 33.6% 44,810234,5502,993 (51.2)6,858 (117.4)0.9Public
Anonymous submission
Tracker MOTAIDF1MTMLFPFNID Sw.FragHzDetector
tianyi
91. using public detections
50.0
±13.7
51.020.5% 35.6% 27,839251,1483,312 (59.7)6,234 (112.3)5.9Public
Anonymous submission
EDMT17
92. using public detections
50.0
±13.9
51.321.6% 36.3% 32,279247,2972,264 (40.3)3,260 (58.0)0.6Public
J. Chen, H. Sheng, Y. Zhang, Z. Xiong. Enhancing Detection Model for Multiple Hypothesis Tracking. In BMTT-PETS CVPRw, 2017.
CoCT
93. online method using public detections
50.1
±12.2
55.623.9% 35.4% 49,887229,3912,075 (35.0)4,304 (72.5)57.8Public
Anonymous submission
Q_ls
94. online method using public detections
50.2
±14.4
43.619.7% 37.3% 23,143253,1514,414 (80.1)6,112 (110.9)1.8Public
Anonymous submission
DEEP_TAMA
95. online method using public detections
50.3
±13.3
53.519.2% 37.5% 25,479252,9962,192 (39.7)3,978 (72.1)1.5Public
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.
DeepMP17
96. using public detections
50.4
±13.1
52.318.8% 38.7% 22,535255,3561,868 (34.1)3,473 (63.4)7.4Public
TLMHT
97. using public detections
50.6
±12.5
56.517.6% 43.4% 22,213255,0301,407 (25.7)2,079 (37.9)2.6Public
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.
TriplDSort
98. using public detections
50.7
±15.3
50.520.3% 36.6% 51,739222,2124,397 (72.5)7,352 (121.3)0.6Public
Anonymous submission
TCT4
99. online method using public detections
50.7
±15.4
50.924.5% 25.6% 46,638224,9556,543 (108.8)7,968 (132.5)14.1Public
Anonymous submission
MHT_DAM
100. using public detections
50.7
±13.7
47.220.8% 36.9% 22,875252,8892,314 (41.9)2,865 (51.9)0.9Public
C. Kim, F. Li, A. Ciptadi, J. Rehg. Multiple Hypothesis Tracking Revisited. In ICCV, 2015.
Tracker MOTAIDF1MTMLFPFNID Sw.FragHzDetector
MOTDT17
101. online method using public detections
50.9
±11.9
52.717.5% 35.7% 24,069250,7682,474 (44.5)5,317 (95.7)18.3Public
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.
STRN_MOT17
102. online method using public detections
50.9
±11.6
56.018.9% 33.8% 25,295249,3652,397 (43.0)9,363 (167.8)13.8Public
J. Xu, Y. Cao, Z. Zhang, H. Hu. Spatial-Temporal Relation Networks for Multi-Object Tracking. In ICCV, 2019.
MOLF
103. online method using public detections
50.9
±12.3
46.718.3% 34.6% 29,398242,8374,535 (79.6)5,343 (93.8)30.5Public
Anonymous submission
DTBasline
104. online method using public detections
51.1
±11.7
53.416.7% 35.5% 20,309253,2452,549 (46.2)5,910 (107.2)22.2Public
Anonymous submission
MOT17ZH
105. online method using public detections
51.1
±13.7
53.416.7% 35.5% 20,309253,2452,549 (46.2)5,910 (107.2)3.7Public
Anonymous submission
STCG17
106. using public detections
51.1
±12.9
54.520.4% 38.6% 32,258241,9161,702 (29.8)2,483 (43.5)66.9Public
Anonymous submission
jCC
107. using public detections
51.2
±14.5
54.520.9% 37.0% 25,937247,8221,802 (32.1)2,984 (53.2)1.8Public
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.
NOTA
108. using public detections
51.3
±11.7
54.517.1% 35.4% 20,148252,5312,285 (41.4)5,798 (105.0)17.8Public
L. Chen, H. Ai, R. Chen, Z. Zhuang. Aggregate Tracklet Appearance Features for Multi-Object Tracking. In IEEE Signal Processing Letters, 2019.
FWT
109. using public detections
51.3
±13.1
47.621.4% 35.2% 24,101247,9212,648 (47.2)4,279 (76.3)0.2Public
R. Henschel, L. Leal-Taixé, D. Cremers, B. Rosenhahn. Fusion of Head and Full-Body Detectors for Multi-Object Tracking. In Trajnet CVPRW, 2018.
AReid17
110. online method using public detections
51.4
±12.2
53.919.2% 32.3% 30,079241,3642,993 (52.3)6,373 (111.4)33.7Public
Anonymous submission
Tracker MOTAIDF1MTMLFPFNID Sw.FragHzDetector
YOONKJ17
111. online method using public detections
51.4
±13.5
54.021.2% 37.3% 29,051243,2022,118 (37.2)3,072 (54.0)3.4Public
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.
ReID_Seq
112. online method using public detections
51.4
±12.7
49.220.3% 34.1% 23,045247,8853,226 (57.5)4,148 (74.0)14.0Public
Anonymous submission
PPMOT17
113. using public detections
51.5
±13.0
47.821.8% 40.1% 19,821251,4952,492 (45.0)2,986 (53.9)35.5Public
Anonymous submission
PP17
114. using public detections
51.5
±13.0
47.821.8% 40.1% 19,821251,4952,492 (45.0)2,986 (53.9)1.9Public
Anonymous submission
overlap
115. using public detections
51.5
±13.1
55.623.0% 36.1% 38,322233,2751,860 (31.7)2,935 (50.0)66.9Public
Anonymous submission
AFN17
116. using public detections
51.5
±13.0
46.920.6% 35.5% 22,391248,4202,593 (46.3)4,308 (77.0)1.8Public
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.
TAR_1
117. online method using public detections
51.6
±11.9
41.421.7% 28.7% 33,514235,8593,629 (62.4)5,949 (102.2)5.6Public
Anonymous submission
SNet_pub
118. online method using public detections
51.7
±12.0
53.418.0% 33.5% 26,809243,0662,735 (48.0)6,157 (108.2)4.9Public
Anonymous submission
eHAF17
119. using public detections
51.8
±13.2
54.723.4% 37.9% 33,212236,7721,834 (31.6)2,739 (47.2)0.7Public
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.
TOPA
120. online method using public detections
51.8
±13.5
53.419.6% 33.1% 27,603241,5462,668 (46.7)5,790 (101.2)443.9Public
Anonymous submission
Tracker MOTAIDF1MTMLFPFNID Sw.FragHzDetector
CMT
121. using public detections
51.8
±12.9
60.719.6% 42.8% 29,528240,9601,217 (21.2)2,008 (35.0)6.5Public
#Submission: TIP-21190-2019
LSMT
122. online method using public detections
51.9
±12.0
53.517.4% 35.0% 18,672250,6622,257 (40.6)5,733 (103.2)8.9Public
Anonymous submission
eTC17
123. using public detections
51.9
±12.4
58.123.1% 35.5% 36,164232,7832,288 (38.9)3,071 (52.3)0.7Public
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.
MOTF17
124. using public detections
52.0
±13.2
50.520.1% 40.4% 19,222249,4642,293 (41.1)3,297 (59.1)2.2Public
Anonymous submission
FAMNet
125. online method using public detections
52.0
±12.0
48.719.1% 33.4% 14,138253,6163,072 (55.8)5,318 (96.6)0.0Public
P. Chu, H. Ling. FAMNet: Joint Learning of Feature, Affinity and Multi-dimensional Assignment for Online Multiple Object Tracking. In ICCV, 2019.
ResTestV2
126. using public detections
52.0
±16.4
50.919.2% 36.3% 33,320234,4812,836 (48.5)4,835 (82.7)66.9Public
Anonymous submission
DAIST_
127. online method using public detections
52.1
±12.5
53.819.7% 33.6% 29,931237,9132,689 (46.5)5,600 (96.8)6.9Public
Anonymous submission
SMOTe
128. online method using public detections
52.1
±12.1
53.818.4% 33.3% 27,571239,7242,691 (46.8)6,134 (106.7)1.0Public
Anonymous submission
PointMOT17
129. using public detections
52.2
±13.3
50.822.4% 40.0% 22,012245,2772,134 (37.8)2,652 (46.9)2.2Public
Anonymous submission
HTBT
130. using public detections
52.3
±13.3
54.522.5% 36.4% 28,743238,2681,959 (33.9)2,973 (51.5)0.4Public
Anonymous submission
Tracker MOTAIDF1MTMLFPFNID Sw.FragHzDetector
TTL
131. online method using public detections
52.3
±12.9
51.319.3% 38.2% 21,617244,5012,779 (49.0)4,290 (75.7)21.5Public
Anonymous submission
PPMOT
132. using public detections
52.4
±13.4
50.822.4% 40.0% 20,176246,1582,224 (39.5)2,769 (49.1)2.3Public
Anonymous submission
TppNoFPN
133. using public detections
52.4
±15.3
52.618.5% 37.2% 18,635247,1042,726 (48.5)5,461 (97.2)4.2Public
Anonymous submission
MOTPP17
134. using public detections
52.4
±15.4
50.822.4% 40.0% 19,922246,1832,223 (39.4)2,769 (49.1)35.5Public
Anonymous submission
OMHT
135. online method using public detections
52.6
±12.9
51.919.3% 38.0% 20,153244,9982,552 (45.1)4,148 (73.3)37.2Public
Anonymous submission
TLO17
136. online method using public detections
52.6
±12.9
51.319.5% 38.2% 20,089244,9302,530 (44.7)4,170 (73.7)25.2Public
Anonymous submission
IDGA
137. using public detections
52.6
±13.4
61.323.6% 40.2% 29,049236,8301,402 (24.2)2,613 (45.0)59.2Public
Anonymous submission
JBNOT
138. using public detections
52.6
±12.3
50.819.7% 35.8% 31,572232,6593,050 (51.9)3,792 (64.5)5.4Public
R. Henschel, Y. Zou, B. Rosenhahn. Multiple People Tracking using Body and Joint Detections. In CVPRW, 2019.
Seq2Seq
139. using public detections
52.7
±12.1
49.417.7% 38.2% 10,819253,8902,396 (43.6)3,374 (61.3)2.6Public
Anonymous submission
LSST17O
140. online method using public detections
52.7
±13.3
57.917.9% 36.6% 22,512241,9362,167 (37.9)7,443 (130.3)1.8Public
Multi-Object Tracking with Multiple Cues and Switcher-Aware Classification
Tracker MOTAIDF1MTMLFPFNID Sw.FragHzDetector
PV
141. online method using public detections
52.8
±14.1
51.819.7% 34.0% 15,884246,9393,711 (66.0)8,757 (155.7)3.5Public
Anonymous submission
MMHT17
142. online method using public detections
52.8
±12.9
53.320.3% 37.5% 25,401238,4132,596 (45.0)4,103 (71.1)37.2Public
Anonymous submission
ENFT17
143. using public detections
52.8
±13.1
57.123.1% 36.8% 26,754237,9091,667 (28.8)2,557 (44.2)0.5Public
BUAA
SMOT_no
144. online method using public detections
52.9
±12.3
54.118.7% 32.8% 26,703236,3462,702 (46.5)6,340 (109.1)4.9Public
Anonymous submission
CRF_TRA
145. using public detections
53.1
±12.2
53.724.2% 30.7% 27,194234,9912,518 (43.2)4,918 (84.3)1.4Public
Anonymous submission
UTA
146. online method using public detections
53.1
±11.7
54.421.5% 31.8% 22,893239,5342,251 (39.1)6,192 (107.6)5.0Public
Anonymous submission
MFT
147. online method using public detections
53.1
±16.1
50.120.4% 39.4% 35,295225,6063,681 (61.3)6,271 (104.5)0.7Public
Anonymous submission
SCNet
148. online method using public detections
53.2
±15.4
54.920.0% 32.1% 30,440231,1092,621 (44.4)6,031 (102.2)1.0Public
Anonymous submission
TLO_MHT
149. online method using public detections
53.3
±12.9
53.320.0% 38.7% 22,161238,9592,434 (42.2)4,089 (70.9)2.0Public
Anonymous submission
MOT_AF
150. online method using public detections
53.5
±13.4
55.619.2% 37.6% 12,867247,8161,672 (29.8)3,516 (62.7)25.2Public
Anonymous submission
Tracker MOTAIDF1MTMLFPFNID Sw.FragHzDetector
Tracktor++
151. online method using public detections
53.5
±14.5
52.319.5% 36.6% 12,201248,0472,072 (37.0)4,611 (82.3)1.5Public
P. Bergmann, T. Meinhardt, L. Leal-Taixé. Tracking without bells and whistles. In ICCV, 2019.
MOT_TBC
152. using public detections
53.9
±15.7
50.020.2% 36.7% 24,584232,6702,945 (50.1)4,612 (78.5)6.7Public
Anonymous submission
HDTR
153. using public detections
54.1
±11.4
48.423.3% 34.8% 18,002238,8181,895 (32.9)2,693 (46.7)1.8Public
M. Babaee, A. Athar, G. Rigoll. Multiple People Tracking Using Hierarchical Deep Tracklet Re-identification. In arXiv preprint arXiv:1811.04091, 2018.
MCLT17
154. using public detections
54.2
±12.3
63.524.0% 38.1% 23,602233,7831,208 (20.6)2,394 (40.9)66.9Public
Anonymous submission
TPM
155. using public detections
54.2
±13.0
52.622.8% 37.5% 13,739242,7301,824 (32.0)2,472 (43.4)0.8Public
Anonymous submission
GNN_tracktor
156. online method using public detections
54.4
±12.9
54.117.8% 37.4% 12,655241,8682,660 (46.6)3,991 (69.9)1.7Public
Anonymous submission
ISDH_HDAv2
157. online method using public detections
54.5
±14.5
65.926.4% 32.1% 46,693207,0933,010 (47.6)6,000 (94.8)3.6Public
MM-008988/ IEEE Transactions on Multimedia
DGCT
158. using public detections
54.5
±13.1
51.321.0% 35.4% 10,471243,1432,865 (50.3)4,889 (85.9)7.0Public
CJY, HYW, KHW @ HRI-SH
LSST17
159. using public detections
54.7
±12.9
62.320.4% 40.1% 26,091228,4341,243 (20.9)3,726 (62.6)1.5Public
Multi-Object Tracking with Multiple Cues and Switcher-Aware Classification
TT17
160. using public detections
54.9
±12.8
63.124.4% 38.1% 20,236233,2951,088 (18.5)2,392 (40.8)2.5Public
TIP-21754-2019
Tracker MOTAIDF1MTMLFPFNID Sw.FragHzDetector
ReTracktor
161. using public detections
55.1
±14.0
52.821.4% 34.9% 15,489235,6942,119 (36.4)4,725 (81.1)0.8Public
Anonymous submission
GMOT
162. using public detections
55.4
±12.2
57.922.7% 34.7% 20,608229,5111,403 (23.7)2,765 (46.6)5.9Public
LXD, KHW @ HRI-SH
MPNTrack17
163. using public detections
55.7
±13.2
59.127.2% 34.4% 25,013223,5311,433 (23.7)3,122 (51.7)4.2Public
Anonymous submission
TARCA
164. online method using public detections
55.9
±13.3
58.124.2% 35.9% 20,141227,1511,784 (29.9)3,741 (62.6)6.9Public
Anonymous submission
TrajTrack
165. online method using public detections
56.0
±12.9
57.222.6% 35.1% 14,378231,2122,546 (43.1)3,452 (58.5)1.4Public
Anonymous submission
DS_MOT
166. online method using public detections
56.1
±15.8
52.721.1% 35.4% 8,866235,4493,609 (61.9)3,777 (64.8)10.0Public
Anonymous submission
Alex1
167. online method using public detections
56.1
±14.9
55.022.1% 34.9% 12,627232,9562,097 (35.7)3,398 (57.9)22.8Public
Anonymous submission
Tracktor++v2
168. online method using public detections
56.3
±13.3
55.121.1% 35.3% 8,866235,4491,987 (34.1)3,763 (64.6)1.5Public
P. Bergmann, T. Meinhardt, L. Leal-Taixé. Tracking without bells and whistles. In ICCV, 2019.
FFT
169. online method using public detections
56.5
±15.7
51.026.2% 26.7% 23,746215,9715,672 (91.9)5,474 (88.7)1.8Public
Anonymous submission
track_bnw
170. online method using public detections
56.7
±13.4
52.123.1% 34.5% 8,895233,2062,351 (40.1)3,155 (53.8)0.7Public
Anonymous submission
Tracker MOTAIDF1MTMLFPFNID Sw.FragHzDetector
track_bin
171. online method using public detections
57.2
±13.3
54.822.6% 34.9% 9,462229,7922,353 (39.7)3,122 (52.7)0.7Public
Anonymous submission
ISE_MOT
172. online method using public detections
58.6
±10.5
54.727.0% 29.8% 23,033208,0452,368 (37.5)3,247 (51.4)16.3Public
Anonymous submission
ISE_MOT17R
173. online method using public detections
60.1
±11.0
56.428.5% 28.1% 23,168199,4832,556 (39.5)3,182 (49.2)7.2Public
MIFT
Lif_T
174. using public detections
60.5
±15.2
65.627.0% 33.6% 14,966206,6191,189 (18.8)3,476 (54.8)0.5Public
Anonymous submission
Response17
175. using public detections
61.2
±14.3
63.236.7% 22.0% 55,168159,9863,589 (50.1)7,640 (106.6)5.9Public
Anonymous submission
SequencesFramesTrajectoriesBoxes
21177572355564228

Difficulty Analysis

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

MOT17-03-SDP

MOT17-03-SDP

(66.8% MOTA)

MOT17-03-FRCNN

MOT17-03-FRCNN

(59.7% MOTA)

MOT17-03-DPM

MOT17-03-DPM

(51.7% MOTA)

...

...

MOT17-14-DPM

MOT17-14-DPM

(21.3% MOTA)

MOT17-14-FRCNN

MOT17-14-FRCNN

(20.6% 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.
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.