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

TrackerMOTAIDF1MTMLFPFN ID Sw.FragHzDetector
TT17
1. 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
Lif_T
2. 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
MCLT17
3. 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
cnt_klt
4. 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
CMT
5. 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
LSST17
6. 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
DS_TW_F
7. 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
IDGA
8. 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
GMOT
9. 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
TLMHT
10. 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.
TrackerMOTAIDF1MTMLFPFN ID Sw.FragHzDetector
OLGT_new
11. 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
MPNTrack17
12. 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
SAS_MOT17
13. 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.
ENFT17
14. 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
MOT_AF
15. 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
STCG17
16. 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
TARCA
17. 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
jCC
18. 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.
TPM
19. 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
eHAF17
20. 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.
TrackerMOTAIDF1MTMLFPFN ID Sw.FragHzDetector
2MPT
21. 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
overlap
22. 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
DeepMP17
23. using public detections
50.4
±13.1
52.318.8% 38.7% 22,535255,3561,868 (34.1)3,473 (63.4)7.4Public
HAM_SADF17
24. 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.
HDTR
25. 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.
HTBT
26. 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
UNS20
27. 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
Tracktor++v2
28. 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.
MHT_bLSTM
29. 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.
Tracktor++
30. 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.
TrackerMOTAIDF1MTMLFPFN ID Sw.FragHzDetector
CoCT
31. 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
Alex1
32. 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
YOONKJ17
33. 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.
ReTracktor
34. 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
PointMOT17
35. 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
DAM_MOT
36. 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
LSST17O
37. 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
DEEP_TAMA
38. 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.
DMAN
39. 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.
AM_ADM17
40. 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.
TrackerMOTAIDF1MTMLFPFN ID Sw.FragHzDetector
MOTPP17
41. 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
PPMOT
42. 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
UTA
43. 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
LSMT
44. 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
EDMT17
45. 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.
AEb_O
46. 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
NOTA
47. 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.
LM_NN
48. 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.
eTC17
49. 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
50. 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
TrackerMOTAIDF1MTMLFPFN ID Sw.FragHzDetector
MHT_DAM
51. 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.
AEb
52. using public detections
48.1
±13.6
46.017.7% 39.5% 16,839273,8192,350 (45.7)5,275 (102.5)66.9Public
track_bnw
53. 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
track_bin
54. 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
55. 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
Seq2Seq
56. 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
STRN_MOT17
57. 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.
TLO_MHT
58. 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
MOTDT17
59. 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.
DSA_MOT17
60. 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
TrackerMOTAIDF1MTMLFPFN ID Sw.FragHzDetector
PPMOT17
61. 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
62. 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
CRF_TRA
63. 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
TLO17
64. 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
SOTD_MC
65. 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
TrajTrack
66. 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
DTBasline
67. 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
68. 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
OMHT
69. 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
ISE_MOT17R
70. 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
TrackerMOTAIDF1MTMLFPFN ID Sw.FragHzDetector
AFN17
71. 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.
MMHT17
72. 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
SCNet
73. 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
lbc_mot
74. 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
FWT
75. 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.
GNN_tracktor
76. 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
TOPA
77. 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
DAIST_
78. 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
79. 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
SMOT_no
80. 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
TrackerMOTAIDF1MTMLFPFN ID Sw.FragHzDetector
TppNoFPN
81. 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
Alex
82. 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
SNet_pub
83. 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
JDT
84. 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
TTL
85. 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
SRPN17
86. 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
ResTestV2
87. 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
DGCT
88. 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
MOT_TBC
89. 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
AReid17
90. 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
TrackerMOTAIDF1MTMLFPFN ID Sw.FragHzDetector
SFS
91. 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
ISDH_HDAv2
92. 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
JBNOT
93. 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.
FAMNet
94. 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.
OST
95. 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
RFTracking
96. 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
GMPHDOGM17
97. 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.
SiaIOU
98. 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
ReID_Seq
99. 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
wangs
100. 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
TrackerMOTAIDF1MTMLFPFN ID Sw.FragHzDetector
zxbtk17
101. 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
tianyi
102. 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
MHT_ReID7
103. 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
RegTL
104. 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
DualAtte
105. 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
OTCD_1
106. 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.
98K
107. 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
Response17
108. 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
DCORV2
109. 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
DS_MOT
110. 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
TrackerMOTAIDF1MTMLFPFN ID Sw.FragHzDetector
CASC_MOT
111. 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
TAR_1
112. 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
E2EM
113. 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
MFT
114. 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
DeepMOTRPN
115. 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
PV
116. 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
Lab031
117. 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
GMPHD_SHA
118. 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.
GMPHD_Rd17
119. 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.
c3d_Track
120. 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
TrackerMOTAIDF1MTMLFPFN ID Sw.FragHzDetector
PHD_GSDL17
121. 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.
LT17
122. 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
EDA_GNN
123. 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
TPbase17
124. 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
SNM17
125. 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
MOT_HY
126. 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
TTracker
127. 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
QiMOT
128. 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
SRPN
129. 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
GNNMOT
130. 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
TrackerMOTAIDF1MTMLFPFN ID Sw.FragHzDetector
KVIOU
131. 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
TriplDSort
132. 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
PHD_GM
133. 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.
Q_ls
134. 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
MASS
135. 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.
EAMTT
136. 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.
MOLF
137. 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
GM_PHD
138. 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.
dcor
139. 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
MOT_BJ
140. 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
TrackerMOTAIDF1MTMLFPFN ID Sw.FragHzDetector
SORT17
141. 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.
GOTURN_3B
142. 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
YoloSort
143. 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
PHD_LMP
144. 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
MOCL
145. 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
JOINT_TRAC
146. 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
D_SST_V1
147. 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
TCT
148. 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
MTDF17
149. 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.
FFT
150. 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
TrackerMOTAIDF1MTMLFPFN ID Sw.FragHzDetector
GMPHD_KCF
151. 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.
IOU17
152. 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.
TCT4
153. 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
GLMBS3
154. 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
FPSN
155. 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.
CGHA_MOT
156. 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
ms_dh
157. 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
DAN__test
158. 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
HISP_DAL17
159. 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.
baitrack
160. 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
TrackerMOTAIDF1MTMLFPFN ID Sw.FragHzDetector
ZM
161. 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
Umot
162. 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
GoturnM17
163. 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
GF
164. 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
HISP_T17
165. 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.
GMPHD_N1Tr
166. 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
167. 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
GMPHD_DAL
168. 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.
cascademot
169. 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
MOTbyReID
170. 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
TrackerMOTAIDF1MTMLFPFN ID Sw.FragHzDetector
XYHv2
171. 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
GM_PHD_D
172. 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
GNNT
173. 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
TM_track
174. 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
CTRACKER
175. 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
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.