MOT16 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
test_trker
1. using public detections
59.7
0.0
±0.0
0.00.0% 100.0% 7182,3260 (nan)0 (nan)22.3Public
Anonymous submission
D_cost16
2. online method using public detections
54.3
39.9
±9.1
35.38.7% 50.2% 1,133107,586790 (19.3)824 (20.1)8.5Public
Anonymous submission
DCOR
3. online method using public detections
65.4
28.3
±9.0
21.73.4% 63.9% 1,618128,345849 (28.7)2,592 (87.5)32.9Public
Anonymous submission
GM_PHD_N1T
4. online method using public detections
71.7
33.3
±8.9
25.55.5% 56.0% 1,750116,4523,499 (96.8)3,594 (99.5)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_e17
5. online method using public detections
73.8
33.8
±8.9
25.36.3% 54.9% 1,766115,1303,778 (102.5)3,874 (105.1)3.3Public
Anonymous submission
GM_PHD_DAL
6. online method using public detections
72.3
35.1
±9.1
26.67.0% 51.4% 2,350111,8864,047 (104.8)5,338 (138.2)3.5Public
N. Baisa. Online Multi-object Visual Tracking using a GM-PHD Filter with Deep Appearance Learning. In 22nd International Conference on Information Fusion, 2019.
GM_PHD_Dl
7. online method using public detections
73.3
34.3
±9.1
20.57.1% 51.5% 2,350111,8865,605 (145.1)5,357 (138.7)3.5Public
Anonymous submission
PV
8. online method using public detections
46.3
50.4
±10.1
50.814.9% 38.9% 2,60086,7801,061 (20.2)3,181 (60.7)7.3Public
Anonymous submission
AMIR
9. online method using public detections
48.6
47.2
±7.7
46.314.0% 41.6% 2,68192,856774 (15.8)1,675 (34.1)1.0Public
A. Sadeghian, A. Alahi, S. Savarese. Tracking The Untrackable: Learning To Track Multiple Cues with Long-Term Dependencies. In ICCV, 2017.
TPM
10. using public detections
36.7
51.3
±9.3
47.918.7% 40.8% 2,70185,504569 (10.7)707 (13.3)0.8Public
Anonymous submission
TrackerAvg RankMOTAIDF1MTML FPFNID Sw.FragHzDetector
KVIOU16
11. using public detections
62.8
33.4
±9.7
32.65.9% 59.6% 2,764117,971760 (21.5)1,473 (41.7)29.6Public
Anonymous submission
JCmin_MOT
12. online method using public detections
56.5
36.7
±9.1
36.27.5% 54.4% 2,936111,890667 (17.3)831 (21.5)14.8Public
M. Abhijeet Boragule. Joint Cost Minimization for Multi-Object Tracking. In 2017 IEEE International Conference on Advanced Vide and Signale Based Surveillance, 2017.
CppSORT
13. online method using public detections
65.3
31.5
±9.0
27.74.3% 59.9% 3,048120,2781,587 (46.6)2,239 (65.8)687.1Public
S. Murray. Real-Time Multiple Object Tracking - A Study on the Importance of Speed. In arXiv preprint arXiv:1709.03572, 2017.
HISP_DAL
14. online method using public detections
69.8
37.4
±8.8
30.57.6% 50.9% 3,222108,8652,101 (52.1)2,151 (53.4)3.3Public
N. Baisa. Robust Online Multi-target Visual Tracking using a HISP Filter with Discriminative Deep Appearance Learning. In CoRR, 2019.
Tracktor16
15. online method using public detections
35.1
54.4
±12.0
52.519.0% 36.9% 3,28079,149682 (12.1)1,480 (26.2)1.5Public
P. Bergmann, T. Meinhardt, L. Leal-Taixé. Tracking without bells and whistles. In ICCV, 2019.
DP_NMS
16. using public detections
55.8
26.2
±9.3
31.24.1% 67.5% 3,689130,557365 (12.9)638 (22.5)5.9Public
H. Pirsiavash, D. Ramanan, C. Fowlkes. Globally-Optimal Greedy Algorithms for Tracking a Variable Number of Objects. In CVPR, 2011.
JPDA_m
17. using public detections
56.4
26.2
±6.1
0.04.1% 67.5% 3,689130,549365 (12.9)638 (22.5)22.2Public
H. Rezatofighi, A. Milan, Z. Zhang, Q. Shi, A. Dick, I. Reid. Joint Probabilistic Data Association Revisited. In ICCV, 2015.
DeepMP16
18. using public detections
34.7
48.7
±10.3
50.115.0% 43.6% 4,11188,862535 (10.4)873 (17.0)9.9Public
Anonymous submission
MTT_TPR
19. using public detections
36.1
54.9
±11.7
53.118.7% 34.8% 4,13076,6731,447 (25.0)3,693 (63.7)6.7Public
Anonymous submission
TBSS
20. online method using public detections
61.1
44.6
±9.3
42.612.3% 43.9% 4,13696,128790 (16.7)1,419 (30.0)3.0Public
X. Zhou, P. Jiang, Z. Wei, H. Dong, F. Wang. Online Multi-Object Tracking with Structural Invariance Constraint. In BMVC, 2018.
TrackerAvg RankMOTAIDF1MTML FPFNID Sw.FragHzDetector
ReTrack16
21. using public detections
31.3
57.0
±12.3
54.221.9% 34.3% 4,44673,258688 (11.5)1,543 (25.8)0.8Public
Anonymous submission
AEb
22. using public detections
39.3
42.9
±11.0
48.715.3% 49.0% 4,48799,310375 (8.2)1,334 (29.3)22.3Public
Anonymous submission
ASTT
23. using public detections
42.4
47.2
±9.6
44.316.3% 41.6% 4,68090,877633 (12.6)814 (16.2)0.5Public
Yi Tao el al., “Adaptive Spatio-temporal Model Based Multiple Object Tracking Considering a Moving Camera[C]”, International Conference on Universal Village (UV), 2018.
HDTR
24. using public detections
26.4
53.6
±8.7
46.621.2% 37.0% 4,71479,353618 (10.9)833 (14.7)3.6Public
LP2D
25. using public detections
61.0
35.7
±10.1
34.28.7% 50.7% 5,084111,163915 (23.4)1,264 (32.4)49.3Public
MOT baseline: Linear programming on 2D image coordinates.
GCRA
26. using public detections
46.1
48.2
±8.3
48.612.9% 41.1% 5,10488,586821 (16.0)1,117 (21.7)2.8Public
C. Ma, C. Yang, F. Yang, Y. Zhuang, Z. Zhang, H. Jia, X. Xie. Trajectory Factory: Tracklet Cleaving and Re-connection by Deep Siamese Bi-GRU for Multiple Object Tracking. In ICME, 2018.
deepS2
27. using public detections
45.4
46.0
±8.2
46.515.5% 42.6% 5,12492,697693 (14.1)759 (15.4)0.7Public
ID 32
DD_TAMA16
28. online method using public detections
42.2
46.2
±8.4
49.414.1% 44.0% 5,12692,367598 (12.1)1,127 (22.8)6.5Public
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.
GMPHD_HDA
29. online method using public detections
56.7
30.5
±6.9
33.44.6% 59.7% 5,169120,970539 (16.0)731 (21.7)13.6Public
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.
MOT_FILTER
30. using public detections
35.8
50.2
±12.9
46.817.9% 39.7% 5,26784,812664 (12.4)978 (18.3)11.8Public
Anonymous submission
TrackerAvg RankMOTAIDF1MTML FPFNID Sw.FragHzDetector
MOTHPCLEAN
31. using public detections
32.2
50.4
±9.4
47.019.1% 39.5% 5,33284,505657 (12.2)862 (16.1)11.8Public
Anonymous submission
HCC
32. using public detections
32.2
49.3
±10.2
50.717.8% 39.9% 5,33386,795391 (7.5)535 (10.2)0.8Public
L. Ma, S. Tang, M. Black, L. Gool. Customized Multi-Person Tracker. In Computer Vision -- ACCV 2018, 2018.
TLO
33. online method using public detections
45.3
50.1
±9.9
48.116.3% 40.7% 5,58284,629786 (14.7)1,294 (24.1)5.6Public
Anonymous submission
DCCRF16
34. online method using public detections
58.4
44.8
±9.8
39.714.1% 42.3% 5,61394,133968 (20.0)1,378 (28.5)0.1Public
H. Zhou, W. Ouyang, J. Cheng, X. Wang, H. Li. Deep Continuous Conditional Random Fields with Asymmetric Inter-object Constraints for Online Multi-object Tracking. In IEEE Transactions on Circuits and Systems for Video Technology, 2018.
OTCD_1
35. online method using public detections
57.8
44.4
±10.8
45.611.6% 47.6% 5,75994,927759 (15.8)1,787 (37.3)17.6Public
Q. Liu, B. Liu, Y. Wu, W. Li, N. Yu. Real-Time Online Multi-Object Tracking in Compressed Domain. In IEEE Access, 2019.
TBD
36. using public detections
81.3
33.7
±9.2
0.07.2% 54.2% 5,804112,5872,418 (63.2)2,252 (58.9)1.3Public
A. Geiger, M. Lauer, C. Wojek, C. Stiller, R. Urtasun. 3D Traffic Scene Understanding from Movable Platforms. In Pattern Analysis and Machine Intelligence (PAMI), 2014.
NLLMPa
37. using public detections
40.4
47.6
±10.6
47.317.0% 40.4% 5,84489,093629 (12.3)768 (15.0)8.3Public
E. Levinkov, J. Uhrig, S. Tang, M. Omran, E. Insafutdinov, A. Kirillov, C. Rother, T. Brox, B. Schiele, B. Andres. Joint Graph Decomposition and Node Labeling: Problem, Algorithms, Applications. In CVPR, 2017.
KCF16
38. online method using public detections
48.8
48.8
±9.6
47.215.8% 38.1% 5,87586,567906 (17.3)1,116 (21.2)0.1Public
P. Chu, H. Fan, C. Tan, H. Ling. Online Multi-Object Tracking with Instance-Aware Tracker and Dynamic Model Refreshment. In WACV, 2019.
OST16
39. online method using public detections
65.7
41.5
±9.2
39.110.7% 45.6% 5,91999,7091,056 (23.3)1,487 (32.8)4.7Public
Anonymous submission
MOTPP16
40. using public detections
33.6
50.5
±9.7
47.219.6% 39.4% 5,93983,694638 (11.8)823 (15.2)3.0Public
Anonymous submission
TrackerAvg RankMOTAIDF1MTML FPFNID Sw.FragHzDetector
TLO16
41. online method using public detections
44.2
49.8
±10.0
47.816.6% 40.6% 6,08584,623782 (14.6)1,278 (23.8)12.4Public
Anonymous submission
MMHT16
42. online method using public detections
41.9
49.9
±9.8
47.316.2% 40.7% 6,11084,455823 (15.3)1,289 (24.0)12.4Public
Anonymous submission
MEN
43. online method using public detections
41.0
50.0
±9.1
52.815.0% 37.0% 6,11784,271706 (13.1)1,797 (33.4)2.0Public
Anonymous submission
OMHT16
44. online method using public detections
44.6
49.8
±9.9
46.716.1% 40.4% 6,24484,342888 (16.5)1,332 (24.8)12.4Public
Anonymous submission
siameseCos
45. using public detections
40.3
49.4
±8.4
49.819.1% 39.4% 6,28185,384679 (12.8)823 (15.5)0.8Public
In preparation
JMC
46. using public detections
47.8
46.3
±9.0
46.315.5% 39.7% 6,37390,914657 (13.1)1,114 (22.2)0.8Public
S. Tang, B. Andres, M. Andriluka, B. Schiele. Multi-Person Tracking by Multicuts and Deep Matching. In BMTT, 2016.
QuadMOT16
47. using public detections
59.8
44.1
±9.4
38.314.6% 44.9% 6,38894,775745 (15.5)1,096 (22.8)1.8Public
J. Son, M. Baek, M. Cho, B. Han. Multi-Object Tracking with Quadruplet Convolutional Neural Networks. In CVPR, 2017.
MHT_DAM
48. using public detections
48.8
45.8
±8.9
46.116.2% 43.2% 6,41291,758590 (11.9)781 (15.7)0.8Public
C. Kim, F. Li, A. Ciptadi, J. Rehg. Multiple Hypothesis Tracking Revisited. In ICCV, 2015.
HISP_T
49. online method using public detections
72.1
35.9
±8.5
28.97.8% 50.1% 6,412107,9182,594 (63.6)2,298 (56.3)4.8Public
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.
CDA_DDALv2
50. online method using public detections
58.3
43.9
±7.8
45.110.7% 44.4% 6,45095,175676 (14.1)1,795 (37.6)0.5Public
S. Bae and K. Yoon, Confidence-Based Data Association and Discriminative Deep Appearance Learning for Robust Online Multi-Object Tracking , In IEEE TPAMI, 2017.
TrackerAvg RankMOTAIDF1MTML FPFNID Sw.FragHzDetector
PHD_GSDL16
51. online method using public detections
67.0
41.0
±8.9
43.111.3% 41.5% 6,49899,2571,810 (39.7)3,650 (80.1)8.3Public
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.
GMMCP
52. using public detections
70.3
38.1
±7.8
35.58.6% 50.9% 6,607105,315937 (22.2)1,669 (39.5)0.5Public
A. Dehghan, S. Assari, M. Shah.. GMMCP-Tracker:Globally Optimal Generalized Maximum Multi Clique Problem for Multiple Object Tracking. In CVPR, 2015.
LFNF16
53. using public detections
61.4
43.6
±11.0
41.613.3% 45.7% 6,61695,363836 (17.5)938 (19.7)0.6Public
Sheng H, Hao L, Chen J, et al. Robust Local Effective Matching Model for Multi-Target Tracking. In PCM, 2017
TLMHT
54. using public detections
40.2
48.7
±8.6
55.315.7% 44.5% 6,63286,504413 (7.9)642 (12.2)4.8Public
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.
oICF
55. online method using public detections
59.0
43.2
±10.2
49.311.3% 48.5% 6,65196,515381 (8.1)1,404 (29.8)0.4Public
H. Kieritz, S. Becker, W. Hübner, M. Arens. Online Multi-Person Tracking using Integral Channel Features. In IEEE Advanced Video and Signal-based Surveillance (AVSS) 2016, 2016.
LMP
56. using public detections
36.8
48.8
±9.8
51.318.2% 40.1% 6,65486,245481 (9.1)595 (11.3)0.5Public
S. Tang, M. Andriluka, B. Andres, B. Schiele. Multiple People Tracking with Lifted Multicut and Person Re-identification. In CVPR, 2017.
MCjoint
57. using public detections
39.6
47.1
±10.8
52.320.4% 46.9% 6,70389,368370 (7.3)598 (11.7)0.6Public
}@article{DBLP:journals/corr/KeuperTYABS16, author = {Margret Keuper and Siyu Tang and Zhongjie Yu and Bjoern Andres and Thomas Brox and Bernt Schiele}, title = {A Multi-cut Formulation for Joint Segmentation and Tracking of Multiple Objects}, journal = {CoRR}, volume = {abs/1607.06317}, year = {2016}, url = {http://arxiv.org/abs/1607.06317}, timestamp = {Wed, 07 Jun 2017 14:41:31 +0200}, biburl = {http://dblp.uni-trier.de/rec/bib/journals/corr/KeuperTYABS16}, bibsource = {dblp computer science bibliography, http://dblp.org} }
CEM
58. using public detections
64.3
33.2
±7.9
0.07.8% 54.4% 6,837114,322642 (17.2)731 (19.6)0.3Public
A. Milan, S. Roth, K. Schindler. Continuous Energy Minimization for Multitarget Tracking. In IEEE TPAMI, 2014.
RAR16pub
59. online method using public detections
58.3
45.9
±9.7
48.813.2% 41.9% 6,87191,173648 (13.0)1,992 (39.8)0.9Public
K. Fang, Y. Xiang, X. Li, S. Savarese. Recurrent Autoregressive Networks for Online Multi-Object Tracking. In The IEEE Winter Conference on Applications of Computer Vision (WACV), 2018.
STCG
60. using public detections
35.9
49.3
±8.6
52.016.2% 41.4% 6,88684,979515 (9.6)775 (14.5)22.3Public
Anonymous submission
TrackerAvg RankMOTAIDF1MTML FPFNID Sw.FragHzDetector
STAM16
61. online method using public detections
56.9
46.0
±9.1
50.014.6% 43.6% 6,89591,117473 (9.5)1,422 (28.4)0.2Public
Q. Chu, W. Ouyang, H. Li, X. Wang, B. Liu, N. Yu. Online Multi-object Tracking Using CNN-Based Single Object Tracker with Spatial-Temporal Attention Mechanism. In 2017 IEEE International Conference on Computer Vision (ICCV), 2017.
HAM_ACT16
62. online method using public detections
54.6
38.1
±8.2
43.37.8% 54.4% 6,976105,434418 (9.9)707 (16.8)8.0Public
retrack
63. online method using public detections new
34.0
53.9
±13.0
52.720.3% 32.3% 6,99976,251818 (14.1)2,613 (44.9)22.3Public
Anonymous submission
MPNTrack16
64. using public detections
20.8
55.9
±11.7
59.926.0% 35.6% 7,08672,902431 (7.2)921 (15.3)11.9Public
Anonymous submission
PHD_T
65. online method using public detections
62.8
40.3
±9.0
48.311.6% 43.1% 7,147100,895815 (18.2)2,446 (54.8)9.9Public
Anonymous submission
GMPHD_ReId
66. online method using public detections new
57.2
40.3
±9.3
48.311.6% 43.1% 7,147100,895815 (18.2)2,446 (54.8)20.4Public
Anonymous submission
CRF_TRACK
67. using public detections
31.6
50.3
±7.9
54.418.3% 35.7% 7,14882,746702 (12.9)1,387 (25.4)1.5Public
Anonymous submission
CRFTrack16
68. using public detections
32.3
50.3
±7.9
54.418.3% 35.7% 7,14882,746702 (12.9)1,387 (25.4)1.5Public
Anonymous submission
LSST16O
69. online method using public detections
46.1
49.2
±10.2
56.513.4% 41.4% 7,18784,875606 (11.3)2,497 (46.7)2.0Public
Anonymous submission
NOTA
70. using public detections
32.8
49.8
±8.3
55.317.9% 37.7% 7,24883,614614 (11.3)1,372 (25.3)19.2Public
L. Chen, H. Ai, R. Chen, Z. Zhuang. Aggregate Tracklet Appearance Features for Multi-Object Tracking. In IEEE Signal Processing Letters, 2019.
TrackerAvg RankMOTAIDF1MTML FPFNID Sw.FragHzDetector
MOTPP
71. using public detections
41.4
48.3
±8.7
45.418.6% 40.1% 7,37886,181661 (12.5)834 (15.8)11.8Public
Anonymous submission
DS_v2
72. using public detections
26.2
59.3
±12.9
57.524.2% 29.1% 7,46565,810887 (13.9)2,738 (42.8)39.4Public
Anonymous submission
UTA
73. online method using public detections
41.7
50.6
±7.9
50.418.3% 33.5% 7,75281,584722 (13.1)2,196 (39.7)5.0Public
Anonymous submission
SRPN16
74. online method using public detections
53.7
48.2
±8.5
51.314.2% 36.8% 7,76785,973790 (14.9)2,006 (38.0)1.4Public
Anonymous submission
LINF1
75. using public detections
56.3
41.0
±9.5
45.711.6% 51.3% 7,89699,224430 (9.4)963 (21.1)4.2Public
L. Fagot-Bouquet, R. Audigier, Y. Dhome, F. Lerasle. Improving Multi-Frame Data Association with Sparse Representations for Robust Near-Online Multi-Object Tracking. In ECCV, 2016.
YOONKJ16
76. online method using public detections
50.8
47.0
±8.4
50.116.5% 41.8% 7,90188,179627 (12.1)945 (18.3)3.5Public
Anonymous submission
DMAN
77. online method using public detections
46.2
46.1
±11.1
54.817.4% 42.7% 7,90989,874532 (10.5)1,616 (31.9)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.
JCSTD
78. online method using public detections
55.1
47.4
±8.3
41.114.4% 36.4% 8,07686,6381,266 (24.1)2,697 (51.4)8.8Public
W. Tian, M. Lauer, L. Chen. Online Multi-Object Tracking Using Joint Domain Information in Traffic Scenarios. In IEEE Transactions on Intelligent Transportation Systems, 2019.
EAMTT_pub
79. online method using public detections
64.7
38.8
±8.5
42.47.9% 49.1% 8,114102,452965 (22.0)1,657 (37.8)11.8Public
R. Sanchez-Matilla, F. Poiesi, A. Cavallaro "Multi-target tracking with strong and weak detections" in BMTT ECCVw 2016
ENFT
80. using public detections
25.1
50.0
±8.2
54.617.8% 41.1% 8,21482,541479 (8.8)724 (13.2)22.3Public
Anonymous submission
TrackerAvg RankMOTAIDF1MTML FPFNID Sw.FragHzDetector
ENFT16
81. using public detections
29.2
50.3
±8.3
55.019.2% 39.8% 8,34181,843490 (8.9)754 (13.7)0.4Public
BUAA
HTBT16
82. using public detections
31.9
50.3
±8.2
55.019.2% 39.8% 8,34181,843490 (8.9)754 (13.7)0.2Public
Anonymous submission
EAGS16
83. using public detections
35.8
47.4
±10.4
50.117.3% 42.7% 8,36986,931575 (11.0)913 (17.5)197.3Public
H. Sheng, X. Zhang, Y. Zhang, Y. Wu, J. Chen. Enhanced Association with Supervoxels in Multiple Hypothesis Tracking. In IEEE Access, 2018.
eTC
84. using public detections
36.5
49.2
±9.1
56.117.3% 40.3% 8,40083,702606 (11.2)882 (16.3)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.
TTL16
85. online method using public detections
44.3
50.4
±10.3
50.117.4% 39.9% 8,49181,156807 (14.5)1,251 (22.5)6.7Public
Anonymous submission
CRF_RNN16
86. using public detections
33.9
49.0
±7.2
53.918.1% 35.8% 8,49583,838621 (11.5)1,252 (23.2)1.5Public
Anonymous submission
AM_ADM
87. online method using public detections
62.3
40.1
±10.1
43.87.1% 46.2% 8,50399,891789 (17.5)1,736 (38.4)5.8Public
S. Lee, M. Kim, S. Bae, Learning Discriminative Appearance Models for Online Multi-Object Tracking with Appearance Discriminability Measures, In IEEE Access, 2018.
RNN_A_P
88. online method using public detections
77.7
34.0
±8.6
33.77.9% 51.0% 8,562109,2692,479 (61.9)3,393 (84.7)19.7Public
Anonymous submission
DpTrack
89. using public detections
30.5
59.3
±18.7
52.827.4% 24.6% 8,56663,6032,045 (31.4)1,555 (23.9)10.4Public
Anonymous submission
FWT
90. using public detections
49.8
47.8
±9.4
44.319.1% 38.2% 8,88685,487852 (16.0)1,534 (28.9)0.6Public
R. Henschel, L. Leal-Taixé, D. Cremers, B. Rosenhahn. Fusion of Head and Full-Body Detectors for Multi-Object Tracking. In Trajnet CVPRW, 2018.
TrackerAvg RankMOTAIDF1MTML FPFNID Sw.FragHzDetector
MOTHP
91. using public detections
37.7
49.1
±9.1
46.920.0% 38.9% 9,03883,031679 (12.5)850 (15.6)11.8Public
Anonymous submission
STRN_MOT16
92. using public detections
45.4
48.5
±8.5
53.917.0% 34.9% 9,03884,178747 (13.9)2,919 (54.2)13.5Public
J. Xu, Y. Cao, Z. Zhang, H. Hu. Spatial-Temporal Relation Networks for Multi-Object Tracking. In ICCV, 2019.
MOTPPF
93. using public detections
36.8
48.4
±8.8
48.519.1% 39.8% 9,15284,266595 (11.1)802 (14.9)11.8Public
Anonymous submission
CMT16
94. using public detections
28.0
49.8
±9.0
59.216.6% 43.6% 9,22981,882365 (6.6)617 (11.2)6.3Public
#Submission: TIP-21190-2019
MOTDT
95. online method using public detections
46.8
47.6
±8.2
50.915.2% 38.3% 9,25385,431792 (14.9)1,858 (35.0)20.6Public
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.
AFN
96. using public detections
41.3
49.0
±10.2
48.219.1% 35.7% 9,50882,506899 (16.4)1,383 (25.3)0.6Public
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.
NOMT
97. using public detections
39.6
46.4
±9.9
53.318.3% 41.4% 9,75387,565359 (6.9)504 (9.7)2.6Public
W. Choi. Near-Online Multi-target Tracking with Aggregated Local Flow Descriptor. In ICCV, 2015.
RTT
98. online method using public detections
47.9
49.9
±8.0
49.319.0% 32.8% 9,92780,406955 (17.1)2,247 (40.2)1.8Public
Anonymous submission
DAST
99. online method using public detections
40.5
48.9
±8.4
53.215.2% 36.2% 9,98782,427838 (15.3)1,936 (35.3)8.7Public
Anonymous submission
PMPTracker
100. online method using public detections
68.5
40.3
±11.7
38.210.4% 42.0% 10,07197,5241,343 (28.9)2,764 (59.4)148.0Public
Light version of PTZ camera Mutiple People Tracker
TrackerAvg RankMOTAIDF1MTML FPFNID Sw.FragHzDetector
AOReid
101. online method using public detections
41.9
48.2
±8.7
50.815.3% 36.8% 10,28383,301821 (15.1)1,963 (36.1)11.2Public
Anonymous submission
SCNet
102. online method using public detections
51.9
50.0
±8.9
51.115.5% 34.1% 10,52679,755866 (15.4)2,141 (38.1)0.3Public
Anonymous submission
pairwise16
103. using public detections
32.0
50.0
±65.9
52.419.4% 38.7% 10,99579,568628 (11.1)939 (16.7)22.3Public
Anonymous submission
EDMT
104. using public detections
47.1
45.3
±9.1
47.917.0% 39.9% 11,12287,890639 (12.3)946 (18.3)1.8Public
J. Chen, H. Sheng, Y. Zhang, Z. Xiong. Enhancing Detection Model for Multiple Hypothesis Tracking. In BMTT-PETS CVPRw, 2017.
SDMT
105. online method using public detections
59.3
39.6
±8.3
42.311.7% 49.1% 11,13098,343602 (13.1)772 (16.8)19.8Public
M. Thoreau, N. Kottege. Deep Similarity Metric Learning for Real-Time Pedestrian Tracking. In arXiv, 2018.
OVBT
106. online method using public detections
80.5
38.4
±8.8
37.87.5% 47.3% 11,51799,4631,321 (29.1)2,140 (47.1)0.3Public
Y. Ban, S. Ba, X. Alameda-Pineda, R. Horaud. Tracking Multiple Persons Based on a Variational Bayesian Model. In BMTT 2016, .
MHT_bLSTM6
107. using public detections
60.5
42.1
±9.7
47.814.9% 44.4% 11,63793,172753 (15.4)1,156 (23.6)1.8Public
C. Kim, F. Li, J. Rehg. Multi-object Tracking with Neural Gating Using Bilinear LSTM. In ECCV, 2018.
LTTSC-CRF
108. using public detections
67.3
37.6
±9.9
42.19.6% 55.2% 11,969101,343481 (10.8)1,012 (22.8)0.6Public
N. Le, A. Heili, M. Odobez. Long-Term Time-Sensitive Costs for CRF-Based Tracking by Detection. In ECCVw, 2016.
MTDF
109. online method using public detections
65.0
45.7
±11.2
40.114.1% 36.4% 12,01884,9701,987 (37.2)3,377 (63.2)1.5Public
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.
dpt_dpt
110. using public detections
24.3
61.3
±10.7
60.432.1% 18.6% 12,41157,481739 (10.8)1,960 (28.6)148.0Public
Anonymous submission
TrackerAvg RankMOTAIDF1MTML FPFNID Sw.FragHzDetector
eHAF16
111. using public detections
41.3
47.2
±16.8
52.418.6% 42.8% 12,58683,107542 (10.0)787 (14.5)0.5Public
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.
TestUnsup
112. online method using public detections
57.8
41.5
±9.0
44.913.7% 43.5% 12,59693,404643 (13.2)796 (16.3)19.7Public
Multi Object Tracking using Deep Structural Cost Minimization in Data Association
MHT_ReID
113. using public detections
63.6
27.1
±47.2
36.430.6% 31.4% 13,068118,8291,071 (30.8)1,141 (32.8)0.5Public
Anonymous submission
INTERA_MOT
114. using public detections
44.2
45.4
±8.6
47.718.1% 38.7% 13,40785,547600 (11.3)930 (17.5)4.3Public
L. Lan, X. Wang, S. Zhang, D. Tao, W. Gao, T. Huang. Interacting Tracklets for Multi-object Tracking. In IEEE Transactions on Image Processing, 2018.
SMOT
115. using public detections
90.6
29.7
±7.3
0.05.3% 47.7% 17,426107,5523,108 (75.8)4,483 (109.3)0.2Public
C. Dicle, O. Camps, M. Sznaier. The Way They Move: Tracking Targets with Similar Appearance. In ICCV, 2013.
GoturnM16
116. online method using public detections
75.7
37.5
±7.5
25.18.4% 46.5% 17,74692,8673,277 (66.8)2,994 (61.0)3.9Public
Anonymous submission
SRPN
117. online method using public detections
63.5
44.0
±10.7
36.615.5% 45.7% 18,78482,3181,047 (19.1)1,118 (20.4)3.9Public
Anonymous submission
MHT___ReID
118. using public detections
40.8
56.4
±11.6
54.239.7% 17.4% 23,79154,1691,478 (21.0)1,547 (22.0)0.5Public
Anonymous submission

Due to a minor bug in the export script, all results were re-evaluated on April 11, 2016. Here is the old snapshot of the leaderboard.


Benchmark Statistics

SequencesFramesTrajectoriesBoxes
75919759182326

Difficulty Analysis

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

MOT16-03

MOT16-03

(53.0% MOTA)

MOT16-06

MOT16-06

(45.7% MOTA)

MOT16-07

MOT16-07

(40.0% MOTA)

...

...

MOT16-08

MOT16-08

(30.6% MOTA)

MOT16-14

MOT16-14

(25.6% 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.