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!

TrackerAvg RankMOTAIDF1MTML FPFNID Sw.FragHzDetector
DS_TW_F
1. online method using public detections
54.0
45.7
±27.0
50.910.8% 75.4% 6,528298,3681,329 (28.2)3,180 (67.5)66.9Public
Anonymous submission
OLGT_new
2. online method using public detections
67.6
45.7
±22.8
49.410.8% 75.5% 6,915298,2881,418 (30.1)3,641 (77.2)6.1Public
Anonymous submission
DGCT
3. using public detections
31.3
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
Tracktor17
4. online method using public detections
45.8
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_AF
5. online method using public detections
30.6
53.5
±13.4
55.619.2% 37.6% 12,867247,8161,672 (29.8)3,516 (62.7)25.2Public
Anonymous submission
TPM
6. using public detections
37.7
54.2
±13.0
52.622.8% 37.5% 13,739242,7301,824 (32.0)2,472 (43.4)0.8Public
Anonymous submission
FAMNet
7. online method using public detections
48.6
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.
ReTracktor
8. using public detections
36.6
55.1
±14.0
52.821.4% 34.9% 15,489235,6942,119 (36.4)4,725 (81.1)0.8Public
Anonymous submission
PV
9. online method using public detections
50.0
52.8
±14.1
51.819.7% 34.0% 15,884246,9393,711 (66.0)8,757 (155.7)3.5Public
Anonymous submission
Alex
10. online method using public detections
70.2
47.6
±14.2
49.813.2% 41.4% 16,028277,1102,731 (53.7)8,481 (166.7)0.2Public
Anonymous submission
TrackerAvg RankMOTAIDF1MTML FPFNID Sw.FragHzDetector
CTRACKER
11. online method using public detections
83.6
39.4
±13.5
26.113.4% 42.5% 16,249307,90017,592 (387.2)14,508 (319.3)66.9Public
Anonymous submission
wangs
12. online method using public detections new
93.5
40.1
±14.0
31.313.4% 42.5% 16,252307,90313,760 (302.9)14,419 (317.4)66.9Public
Anonymous submission
AEb_O
13. online method using public detections
63.9
46.4
±13.9
44.916.5% 41.4% 17,030283,2652,266 (45.5)5,053 (101.5)1.8Public
Anonymous submission
HDTR
14. using public detections
30.4
54.1
±11.4
48.423.3% 34.8% 18,002238,8181,895 (32.9)2,693 (46.7)1.8Public
GM_PHD
15. online method using public detections
87.6
42.1
±13.0
33.911.9% 42.7% 18,214297,64610,698 (226.4)10,864 (229.9)9.9Public
Anonymous submission
GMPHD_N1Tr
16. online method using public detections
88.2
42.1
±13.5
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.
OTCD_1
17. online method using public detections
65.6
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.
TppNoFPN
18. using public detections
52.1
52.4
±15.3
52.618.5% 37.2% 18,635247,1042,726 (48.5)5,461 (97.2)4.2Public
Anonymous submission
LSMT
19. online method using public detections
41.8
51.9
±12.0
53.517.4% 35.0% 18,672250,6622,257 (40.6)5,733 (103.2)8.9Public
Anonymous submission
D_SST_V1
20. online method using public detections
86.8
42.7
±13.9
46.111.8% 44.4% 18,861298,9895,531 (117.7)13,775 (293.0)2.3Public
Anonymous submission
TrackerAvg RankMOTAIDF1MTML FPFNID Sw.FragHzDetector
QiMOT
21. online method using public detections
78.8
47.2
±13.1
40.815.5% 39.9% 18,907274,8284,320 (84.2)5,917 (115.4)1.8Public
Anonymous submission
GM_PHD_D
22. online method using public detections
83.0
44.0
±13.8
34.214.8% 39.4% 19,135283,53013,556 (272.5)13,821 (277.8)9.9Public
Anonymous submission
GMPHD_DAL
23. online method using public detections
84.7
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 22nd International Conference on Information Fusion, 2019.
MOTF17
24. using public detections
49.8
52.0
±13.2
50.520.1% 40.4% 19,222249,4642,293 (41.1)3,297 (59.1)2.2Public
Anonymous submission
PPMOT17
25. using public detections
45.0
51.5
±13.0
47.821.8% 40.1% 19,821251,4952,492 (45.0)2,986 (53.9)35.5Public
Anonymous submission
MOTPP17
26. using public detections
36.2
52.4
±15.4
50.822.4% 40.0% 19,922246,1832,223 (39.4)2,769 (49.1)35.5Public
Anonymous submission
IOU17
27. using public detections
72.8
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.
TLO17
28. online method using public detections
43.0
52.6
±12.9
51.319.5% 38.2% 20,089244,9302,530 (44.7)4,170 (73.7)37.2Public
Anonymous submission
NOTA
29. using public detections
44.7
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.
OMHT
30. online method using public detections
40.3
52.6
±12.9
51.919.3% 38.0% 20,153244,9982,552 (45.1)4,148 (73.3)37.2Public
Anonymous submission
TrackerAvg RankMOTAIDF1MTML FPFNID Sw.FragHzDetector
PPMOT
31. using public detections
46.2
52.4
±13.4
50.822.4% 40.0% 20,176246,1582,224 (39.5)2,769 (49.1)2.3Public
Anonymous submission
PP17
32. using public detections
45.7
52.4
±13.4
50.822.4% 40.0% 20,183245,9982,215 (39.3)2,752 (48.8)1.9Public
Anonymous submission
TT17
33. using public detections
31.2
54.9
±12.8
63.124.4% 38.1% 20,236233,2951,088 (18.5)2,392 (40.8)2.5Public
Anonymous submission
DTBasline
34. online method using public detections
42.7
51.1
±11.7
53.416.7% 35.5% 20,309253,2452,549 (46.2)5,910 (107.2)22.2Public
Anonymous submission
MOT17ZH
35. online method using public detections
52.7
51.1
±13.7
53.416.7% 35.5% 20,309253,2452,549 (46.2)5,910 (107.2)3.7Public
Anonymous submission
GMOT
36. using public detections
19.3
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
E2EM
37. online method using public detections
61.9
47.5
±14.5
48.816.5% 37.5% 20,655272,1873,632 (70.2)12,712 (245.6)29.6Public
Anonymous submission
AEb
38. using public detections
36.0
49.5
±13.6
52.020.8% 38.9% 20,822262,2381,779 (33.2)4,792 (89.5)66.9Public
Anonymous submission
RegTL
39. using public detections
65.0
48.1
±13.7
41.618.1% 39.8% 20,850268,3633,386 (64.6)4,524 (86.3)17.8Public
Anonymous submission
HAM_SADF17
40. online method using public detections
55.2
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.
TrackerAvg RankMOTAIDF1MTML FPFNID Sw.FragHzDetector
SRPN17
41. online method using public detections
55.6
51.0
±11.7
53.516.8% 35.1% 21,011252,8082,596 (47.0)5,981 (108.4)4.1Public
Anonymous submission
CASC_MOT
42. online method using public detections
81.9
42.3
±12.8
46.89.1% 44.1% 21,035300,7973,616 (77.4)16,656 (356.7)11.4Public
Anonymous submission
DCORV2
43. online method using public detections
71.9
45.5
±13.9
36.114.6% 40.4% 21,161282,9013,592 (72.0)7,696 (154.4)35.5Public
Anonymous submission
DSA_MOT17
44. online method using public detections
62.3
45.0
±12.6
43.615.8% 39.2% 21,442286,4822,491 (50.6)3,824 (77.7)9.9Public
Anonymous submission
TTL
45. online method using public detections
49.2
52.3
±12.9
51.319.3% 38.2% 21,617244,5012,779 (49.0)4,290 (75.7)21.5Public
Anonymous submission
OST
46. using public detections
61.4
49.7
±14.0
50.417.0% 36.7% 21,811258,6493,077 (56.8)4,339 (80.1)1.7Public
Anonymous submission
HISP_DAL17
47. online method using public detections
81.8
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.
PointMOT17
48. using public detections
45.9
52.2
±13.3
50.822.4% 40.0% 22,012245,2772,134 (37.8)2,652 (46.9)2.2Public
Anonymous submission
MHT_ReID7
49. using public detections
75.9
46.5
±13.7
46.918.8% 40.3% 22,203276,3743,386 (66.4)8,521 (167.0)1.6Public
Anonymous submission
TLMHT
50. using public detections
53.8
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.
TrackerAvg RankMOTAIDF1MTML FPFNID Sw.FragHzDetector
GF
51. online method using public detections
78.1
45.0
±13.9
39.115.0% 39.0% 22,387277,33510,397 (204.5)7,421 (145.9)9.9Public
Anonymous submission
AFN17
52. using public detections
44.0
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.
LSST17O
53. online method using public detections
46.9
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
DeepMP17
54. using public detections
43.8
50.4
±13.1
52.318.8% 38.7% 22,535255,3561,868 (34.1)3,473 (63.4)7.4Public
MHT_DAM
55. using public detections
51.5
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.
ReID_Seq
56. online method using public detections
47.3
51.4
±12.7
49.220.3% 34.1% 23,045247,8853,226 (57.5)4,148 (74.0)14.0Public
Anonymous submission
Q_ls
57. online method using public detections
67.2
50.2
±14.4
43.619.7% 37.3% 23,143253,1514,414 (80.1)6,112 (110.9)1.8Public
Anonymous submission
PHD_GSDL17
58. online method using public detections
67.4
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.
GNNMOT
59. online method using public detections
78.9
42.0
±12.8
29.312.0% 50.0% 23,294299,6944,377 (93.4)3,847 (82.1)177.6Public
Anonymous submission
GM_PHD
60. online method using public detections
90.5
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.
TrackerAvg RankMOTAIDF1MTML FPFNID Sw.FragHzDetector
GMPHDOGM17
61. online method using public detections
48.4
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.
MOTDT17
62. online method using public detections
48.0
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.
FWT
63. using public detections
49.3
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.
TLO_MHT
64. online method using public detections
42.9
52.9
±12.9
53.320.1% 37.6% 24,142238,8342,565 (44.5)4,101 (71.1)20.7Public
Anonymous submission
UTA
65. online method using public detections
44.3
53.0
±11.6
52.221.7% 31.5% 24,468238,3562,292 (39.7)6,231 (107.9)5.0Public
Anonymous submission
RFTracking
66. online method using public detections new
63.2
48.5
±14.8
43.617.7% 39.0% 24,544262,7653,160 (59.1)5,097 (95.4)66.9Public
Anonymous submission
MOT_TBC
67. using public detections
44.3
53.9
±15.7
50.020.2% 36.7% 24,584232,6702,945 (50.1)4,612 (78.5)6.7Public
Anonymous submission
DualAtte
68. online method using public detections
67.8
48.4
±14.5
43.717.6% 39.0% 24,915262,6543,423 (64.0)5,192 (97.1)0.3Public
Anonymous submission
MPNTrack17
69. using public detections
26.5
55.7
±13.2
59.127.2% 34.4% 25,013223,5311,433 (23.7)3,122 (51.7)4.2Public
Anonymous submission
AM_ADM17
70. online method using public detections
57.4
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.
TrackerAvg RankMOTAIDF1MTML FPFNID Sw.FragHzDetector
ZM
71. online method using public detections
93.3
43.5
±13.9
32.614.5% 39.9% 25,083284,4059,197 (185.4)8,849 (178.4)14.4Public
Anonymous submission
SNM17
72. online method using public detections
83.7
46.8
±13.8
43.416.2% 37.1% 25,104271,0424,213 (81.1)9,891 (190.3)0.8Public
Anonymous submission
STRN_MOT17
73. online method using public detections
52.0
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.
MMHT17
74. online method using public detections
38.1
52.8
±12.9
53.320.3% 37.5% 25,401238,4132,596 (45.0)4,103 (71.1)37.2Public
Anonymous submission
CGHA_MOT
75. online method using public detections
89.5
41.2
±14.1
44.08.3% 45.9% 25,462299,1127,294 (155.2)18,655 (397.0)11.4Public
Anonymous submission
HISP_T17
76. online method using public detections
84.0
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.
DEEP_TAMA
77. online method using public detections
47.6
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.
EDA_GNN
78. online method using public detections
71.0
45.5
±13.8
40.515.6% 40.6% 25,685277,6634,091 (80.5)5,579 (109.8)39.3Public
Paper ID 2713
MASS
79. online method using public detections
71.8
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.
GMPHD_SHA
80. online method using public detections
82.6
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.
TrackerAvg RankMOTAIDF1MTML FPFNID Sw.FragHzDetector
jCC
81. using public detections
42.5
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.
MHT_bLSTM
82. using public detections
61.7
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.
LSST17
83. using public detections
39.4
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
DMAN
84. online method using public detections
55.3
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.
PHD_GM
85. online method using public detections
62.9
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.
DeepMOTRPN
86. online method using public detections
65.0
48.1
±14.5
43.017.6% 38.6% 26,490262,5783,696 (69.1)5,353 (100.1)4.9Public
Anonymous submission
SMOT_no
87. online method using public detections
48.8
52.9
±12.3
54.118.7% 32.8% 26,703236,3462,702 (46.5)6,340 (109.1)4.9Public
Anonymous submission
ENFT17
88. using public detections
33.0
52.8
±13.1
57.123.1% 36.8% 26,754237,9091,667 (28.8)2,557 (44.2)0.5Public
BUAA
SNet_pub
89. online method using public detections
53.8
51.7
±12.0
53.418.0% 33.5% 26,809243,0662,735 (48.0)6,157 (108.2)4.9Public
Anonymous submission
SiaIOU
90. using public detections
64.3
48.5
±16.7
48.518.9% 38.8% 26,867260,2783,152 (58.5)4,391 (81.5)8.3Public
Anonymous submission
TrackerAvg RankMOTAIDF1MTML FPFNID Sw.FragHzDetector
JDT
91. online method using public detections
61.8
47.4
±12.2
50.116.8% 37.2% 26,910267,3312,760 (52.5)6,211 (118.0)35.1Public
Anonymous submission
CRF_TRA
92. using public detections
33.8
53.1
±12.2
53.724.2% 30.7% 27,194234,9912,518 (43.2)4,918 (84.3)1.4Public
Anonymous submission
SMOTe
93. online method using public detections
54.9
52.1
±12.1
53.818.4% 33.3% 27,571239,7242,691 (46.8)6,134 (106.7)1.0Public
Anonymous submission
TOPA
94. online method using public detections
44.2
51.8
±13.5
53.419.6% 33.1% 27,603241,5462,668 (46.7)5,790 (101.2)443.9Public
Anonymous submission
TM_track
95. online method using public detections
105.3
41.1
±14.9
32.813.2% 41.3% 27,606287,51117,408 (355.0)15,197 (309.9)2.5Public
Anonymous submission
cascademot
96. online method using public detections
85.3
41.8
±16.0
34.215.2% 38.7% 27,816288,76811,535 (236.3)14,800 (303.2)17.8Public
Anonymous submission
LT17
97. online method using public detections
70.5
47.7
±16.6
45.217.3% 36.0% 27,856263,0624,042 (75.7)9,183 (172.0)7.2Public
Anonymous submission
PHD_LMP
98. online method using public detections
77.8
45.9
±13.1
42.515.5% 37.9% 27,946272,1964,977 (96.2)6,985 (135.0)29.4Public
Anonymous submission
SORT17
99. online method using public detections
88.5
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.
Umot
100. online method using public detections
91.4
43.9
±13.8
37.815.2% 38.9% 28,596278,6219,363 (185.0)11,371 (224.6)19.7Public
Anonymous submission
TrackerAvg RankMOTAIDF1MTML FPFNID Sw.FragHzDetector
HTBT
101. using public detections
40.1
52.3
±13.3
54.522.5% 36.4% 28,743238,2681,959 (33.9)2,973 (51.5)0.4Public
Anonymous submission
DAM_MOT
102. online method using public detections
52.3
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
IDGA
103. using public detections
29.4
52.6
±13.4
61.323.6% 40.2% 29,049236,8301,402 (24.2)2,613 (45.0)59.2Public
Anonymous submission
YOONKJ17
104. online method using public detections
49.6
51.4
±13.5
54.021.2% 37.3% 29,051243,2022,118 (37.2)3,072 (54.0)3.4Public
Anonymous submission
SAS_MOT17
105. using public detections
67.9
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.
CMT
106. using public detections
37.3
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
XYHv2
107. online method using public detections
106.5
39.9
±12.4
23.89.9% 41.8% 29,713296,70412,900 (272.1)12,911 (272.3)7.8Public
Anonymous submission
AReid17
108. online method using public detections
39.8
51.4
±12.2
53.919.2% 32.3% 30,079241,3642,993 (52.3)6,373 (111.4)33.7Public
Anonymous submission
dcor
109. online method using public detections
75.8
45.0
±14.2
34.015.4% 38.2% 30,231275,2654,801 (93.7)8,498 (165.9)44.4Public
Anonymous submission
GOTURN_3B
110. online method using public detections
81.5
44.3
±13.7
38.513.0% 43.2% 30,302279,1444,861 (96.2)5,277 (104.4)48.6Public
Anonymous submission
TrackerAvg RankMOTAIDF1MTML FPFNID Sw.FragHzDetector
DAN__test
111. using public detections
91.4
43.0
±14.7
43.313.5% 40.0% 30,367283,5337,576 (152.3)14,990 (301.3)1.8Public
Anonymous submission
SCNet
112. online method using public detections
49.1
53.2
±15.4
54.920.0% 32.1% 30,440231,1092,621 (44.4)6,031 (102.2)1.0Public
Anonymous submission
2MPT
113. using public detections
52.0
48.1
±14.2
52.917.4% 39.6% 30,650260,1331,860 (34.5)2,784 (51.7)2.7Public
Anonymous submission
EAMTT
114. online method using public detections
84.1
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.
JBNOT
115. using public detections
45.6
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.
Lab031
116. using public detections
70.6
46.9
±16.2
48.117.7% 36.1% 31,634263,9383,795 (71.3)10,498 (197.3)9.4Public
Anonymous submission
ms_dh
117. online method using public detections
96.8
42.6
±14.6
40.113.6% 40.0% 31,878284,5287,446 (150.2)14,736 (297.3)4.0Public
Anonymous submission
STCG17
118. using public detections
40.6
51.1
±12.9
54.520.4% 38.6% 32,258241,9161,702 (29.8)2,483 (43.5)66.9Public
Anonymous submission
EDMT17
119. using public detections
49.4
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.
98K
120. using public detections
74.5
40.8
±17.2
37.015.6% 38.1% 32,312298,1743,514 (74.5)4,991 (105.8)17.7Public
Anonymous submission
TrackerAvg RankMOTAIDF1MTML FPFNID Sw.FragHzDetector
zxbtk17
121. online method using public detections
77.7
45.1
±14.7
40.017.7% 31.8% 33,186273,5313,303 (64.1)8,148 (158.1)8.3Public
Anonymous submission
eHAF17
122. using public detections
39.6
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.
c3d_Track
123. online method using public detections
88.0
41.5
±13.7
40.210.7% 48.5% 33,332292,9313,890 (80.9)11,454 (238.2)22.2Public
Anonymous submission
TAR_1
124. online method using public detections
57.8
51.6
±11.9
41.421.7% 28.7% 33,514235,8593,629 (62.4)5,949 (102.2)5.6Public
Anonymous submission
SOTD_MC
125. online method using public detections
57.3
48.4
±15.0
45.519.4% 35.9% 33,525255,0912,531 (46.2)4,944 (90.2)67.0Public
Anonymous submission
FPSN
126. online method using public detections
76.3
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.
KVIOU
127. online method using public detections
72.7
46.6
±14.2
44.017.3% 38.1% 34,838262,0084,379 (81.8)7,844 (146.4)29.6Public
Anonymous submission
MFT
128. online method using public detections
61.3
53.1
±16.6
50.120.4% 39.4% 35,295225,6063,681 (61.3)6,271 (104.5)0.7Public
Anonymous submission
MOTbyReID
129. online method using public detections
87.9
43.6
±13.7
37.117.6% 36.3% 35,725270,03612,347 (236.8)11,408 (218.8)2.5Public
Anonymous submission
eTC17
130. using public detections
38.8
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.
TrackerAvg RankMOTAIDF1MTML FPFNID Sw.FragHzDetector
MTDF17
131. online method using public detections
71.3
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.
GMPHD_Rd17
132. online method using public detections
63.5
46.3
±14.7
50.819.0% 33.8% 37,249260,8714,742 (88.2)8,636 (160.6)20.1Public
Anonymous submission
SRPN
133. online method using public detections
79.5
47.8
±13.2
41.417.0% 41.7% 38,279251,9894,325 (78.2)5,355 (96.8)11.8Public
Anonymous submission
overlap
134. using public detections
33.4
51.5
±13.1
55.623.0% 36.1% 38,322233,2751,860 (31.7)2,935 (50.0)66.9Public
Anonymous submission
GLMBS3
135. using public detections
95.0
38.0
±13.7
32.39.3% 52.8% 38,874304,0166,963 (151.0)3,927 (85.2)4.9Public
Anonymous submission
TTracker
136. online method using public detections
69.1
46.2
±14.0
44.021.0% 35.6% 44,854254,4384,258 (77.6)6,307 (114.9)29.6Public
Anonymous submission
ISDH_HDAv2
137. online method using public detections
39.5
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
MOT_HY
138. using public detections
71.1
47.3
±121.2
49.417.2% 33.8% 46,875246,0614,231 (75.0)8,188 (145.2)2.0Public
Anonymous submission
TPbase17
139. online method using public detections
81.8
43.3
±15.0
48.216.2% 36.6% 49,992265,8154,194 (79.3)12,103 (228.8)22.2Public
Anonymous submission
GMPHD_KCF
140. online method using public detections
98.5
39.6
±13.6
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.
TrackerAvg RankMOTAIDF1MTML FPFNID Sw.FragHzDetector
MOT_BJ
141. online method using public detections
111.5
-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
GoturnM17
142. online method using public detections
99.4
38.3
±9.0
25.79.4% 47.1% 55,381282,67010,328 (207.0)9,849 (197.4)11.8Public
Anonymous submission
cnt_klt
143. using public detections
31.6
48.0
±11.8
57.819.0% 31.6% 63,207228,7831,215 (20.4)4,134 (69.5)59.2Public
Anonymous submission
ResTestV2
144. using public detections
90.1
40.0
±16.7
36.912.1% 39.5% 71,781261,0725,718 (106.4)9,675 (180.1)66.9Public
Anonymous submission
baitrack
145. using public detections
68.0
37.6
±19.4
20.321.0% 30.9% 99,085244,0018,808 (155.2)6,708 (118.2)6.4Public
Anonymous submission
JOINT_TRAC
146. using public detections
90.1
29.4
±17.6
34.512.6% 42.3% 132,192260,8085,397 (100.4)10,704 (199.0)66.9Public
Anonymous submission
YoloSort
147. online method using public detections
79.6
29.5
±24.1
41.715.0% 36.4% 154,747238,2414,888 (84.6)4,952 (85.7)14.4Public
Anonymous submission

Benchmark Statistics

SequencesFramesTrajectoriesBoxes
21177572355564228

Difficulty Analysis

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

MOT17-03-SDP

MOT17-03-SDP

(70.8% MOTA)

MOT17-03-FRCNN

MOT17-03-FRCNN

(58.2% MOTA)

MOT17-06-SDP

MOT17-06-SDP

(49.0% MOTA)

...

...

MOT17-14-DPM

MOT17-14-DPM

(19.4% MOTA)

MOT17-14-FRCNN

MOT17-14-FRCNN

(18.7% MOTA)


Evaluation Measures

Lower is better. Higher is better.
Measure Better Perfect Description
Avg Rank lower 1 This is the rank of each tracker averaged over all present evaluation measures.
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.

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.