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!


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

TrackerMOTAIDF1MTMLFPFNID Sw.FragDetector
Response
1. using public detections
62.0
±0.0
63.837.7% 20.7% 18,30850,039909 (12.5)2,009 (27.7)Public
Anonymous submission
dpt_dpt
2. using public detections
61.3
±0.0
60.432.1% 18.6% 12,41157,481739 (10.8)1,960 (28.6)Public
Anonymous submission
Lif_T
3. online method using public detections
61.3
±0.0
64.727.0% 34.0% 4,84465,401389 (6.1)1,034 (16.1)Public
Anonymous submission
TrkReid
4. using public detections
60.2
±0.0
54.330.7% 20.2% 11,36859,5371,594 (23.7)1,957 (29.1)Public
Anonymous submission
ISE_MOT16
5. using public detections
60.1
±0.0
56.926.1% 29.1% 6,96465,044739 (11.5)951 (14.8)Public
MIFT
DS_v2
6.
59.3
±0.0
57.524.2% 29.1% 7,46565,810887 (13.9)2,738 (42.8)Public
Anonymous submission
DpTrack
7. using public detections
59.3
±0.0
52.827.4% 24.6% 8,56663,6032,045 (31.4)1,555 (23.9)Public
Anonymous submission
SORT_Alex
8. online method using public detections
58.4
±0.0
56.324.2% 32.9% 3,43671,846604 (10.0)808 (13.3)Public
Anonymous submission
ReTrack16
9. online method
57.0
±0.0
54.221.9% 34.3% 4,44673,258688 (11.5)1,543 (25.8)Public
Anonymous submission
FFT16
10.
56.5
±0.0
50.123.6% 29.4% 5,83171,8251,635 (27.0)1,607 (26.5)Public
Anonymous submission
TrackerMOTAIDF1MTMLFPFNID Sw.FragDetector
MHT___ReID
11.
56.4
±0.0
54.239.7% 17.4% 23,79154,1691,478 (21.0)1,547 (22.0)Public
Anonymous submission
MPNTrack16
12. online method using public detections
55.9
±0.0
59.926.0% 35.6% 7,08672,902431 (7.2)921 (15.3)Public
Anonymous submission
MTT_TPR
13. online method using public detections
54.9
±0.0
53.118.7% 34.8% 4,13076,6731,447 (25.0)3,693 (63.7)Public
Anonymous submission
Tracktor16
14. using public detections
54.4
±0.0
52.519.0% 36.9% 3,28079,149682 (12.1)1,480 (26.2)Public
P. Bergmann, T. Meinhardt, L. Leal-Taixé. Tracking without bells and whistles. In ICCV, 2019.
retrack
15. online method using public detections
53.9
±0.0
52.720.3% 32.3% 6,99976,251818 (14.1)2,613 (44.9)Public
Anonymous submission
moT_
16. online method using public detections
53.6
±0.0
52.619.9% 32.3% 6,03877,801764 (13.3)2,905 (50.7)Public
Anonymous submission
HDTR
17. online method using public detections
53.6
±0.0
46.621.2% 37.0% 4,71479,353618 (10.9)833 (14.7)Public
MLT
18. online method
52.8
±0.0
62.621.1% 42.4% 5,36280,444299 (5.4)702 (12.6)Public
Anonymous submission
TCT5
19. using public detections
51.9
±0.0
51.825.4% 24.9% 15,62270,5571,494 (24.4)2,466 (40.2)Public
Anonymous submission
TPM
20. using public detections
51.3
±0.0
47.918.7% 40.8% 2,70185,504569 (10.7)707 (13.3)Public
Anonymous submission
TrackerMOTAIDF1MTMLFPFNID Sw.FragDetector
MOTHYPER
21.
50.9
±0.0
47.419.4% 39.4% 4,86684,022619 (11.5)814 (15.1)Public
Anonymous submission
UTA
22. online method using public detections
50.6
±0.0
50.418.3% 33.5% 7,75281,584722 (13.1)2,196 (39.7)Public
Anonymous submission
MOTPP16
23. online method
50.5
±0.0
47.219.6% 39.4% 5,93983,694638 (11.8)823 (15.2)Public
Anonymous submission
PV
24. online method
50.4
±0.0
50.814.9% 38.9% 2,60086,7801,061 (20.2)3,181 (60.7)Public
Anonymous submission
TTL16
25. using public detections
50.4
±0.0
50.117.4% 39.9% 8,49181,156807 (14.5)1,251 (22.5)Public
Anonymous submission
MOTHPCLEAN
26.
50.4
±0.0
47.019.1% 39.5% 5,33284,505657 (12.2)862 (16.1)Public
Anonymous submission
CRFTrack16
27. using public detections
50.3
±0.0
54.418.3% 35.7% 7,14882,746702 (12.9)1,387 (25.4)Public
Anonymous submission
CRF_TRACK
28. online method
50.3
±0.0
54.418.3% 35.7% 7,14882,746702 (12.9)1,387 (25.4)Public
Anonymous submission
ENFT16
29.
50.3
±0.0
55.019.2% 39.8% 8,34181,843490 (8.9)754 (13.7)Public
BUAA
HTBT16
30. online method using public detections
50.3
±0.0
55.019.2% 39.8% 8,34181,843490 (8.9)754 (13.7)Public
Anonymous submission
TrackerMOTAIDF1MTMLFPFNID Sw.FragDetector
MOT_FILTER
31. using public detections
50.2
±0.0
46.817.9% 39.7% 5,26784,812664 (12.4)978 (18.3)Public
Anonymous submission
TLO
32. online method using public detections
50.1
±0.0
48.116.3% 40.7% 5,58284,629786 (14.7)1,294 (24.1)Public
Anonymous submission
MEN
33. online method using public detections
50.0
±0.0
52.815.0% 37.0% 6,11784,271706 (13.1)1,797 (33.4)Public
Anonymous submission
SCNet
34. using public detections
50.0
±0.0
51.115.5% 34.1% 10,52679,755866 (15.4)2,141 (38.1)Public
Anonymous submission
pairwise16
35.
50.0
±0.0
52.419.4% 38.7% 10,99579,568628 (11.1)939 (16.7)Public
Anonymous submission
ENFT
36. online method
50.0
±0.0
54.617.8% 41.1% 8,21482,541479 (8.8)724 (13.2)Public
Anonymous submission
RTT
37. online method using public detections
49.9
±0.0
49.319.0% 32.8% 9,92780,406955 (17.1)2,247 (40.2)Public
Anonymous submission
MMHT16
38. using public detections
49.9
±0.0
47.316.2% 40.7% 6,11084,455823 (15.3)1,289 (24.0)Public
Anonymous submission
OMHT16
39. using public detections
49.8
±0.0
46.716.1% 40.4% 6,24484,342888 (16.5)1,332 (24.8)Public
Anonymous submission
CMT16
40. using public detections
49.8
±0.0
59.216.6% 43.6% 9,22981,882365 (6.6)617 (11.2)Public
#Submission: TIP-21190-2019
TrackerMOTAIDF1MTMLFPFNID Sw.FragDetector
NOTA
41. online method using public detections
49.8
±0.0
55.317.9% 37.7% 7,24883,614614 (11.3)1,372 (25.3)Public
L. Chen, H. Ai, R. Chen, Z. Zhuang. Aggregate Tracklet Appearance Features for Multi-Object Tracking. In IEEE Signal Processing Letters, 2019.
TLO16
42. using public detections
49.8
±0.0
47.816.6% 40.6% 6,08584,623782 (14.6)1,278 (23.8)Public
Anonymous submission
siameseCos
43.
49.4
±0.0
49.819.1% 39.4% 6,28185,384679 (12.8)823 (15.5)Public
In preparation
STCG
44. online method
49.3
±0.0
52.016.2% 41.4% 6,88684,979515 (9.6)775 (14.5)Public
Anonymous submission
HCC
45. online method using public detections
49.3
±0.0
50.717.8% 39.9% 5,33386,795391 (7.5)535 (10.2)Public
L. Ma, S. Tang, M. Black, L. Gool. Customized Multi-Person Tracker. In Computer Vision -- ACCV 2018, 2018.
LSST16O
46. online method using public detections
49.2
±0.0
56.513.4% 41.4% 7,18784,875606 (11.3)2,497 (46.7)Public
Anonymous submission
eTC
47. using public detections
49.2
±0.0
56.117.3% 40.3% 8,40083,702606 (11.2)882 (16.3)Public
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.
MOTHP
48.
49.1
±0.0
46.920.0% 38.9% 9,03883,031679 (12.5)850 (15.6)Public
Anonymous submission
AFN
49. online method using public detections
49.0
±0.0
48.219.1% 35.7% 9,50882,506899 (16.4)1,383 (25.3)Public
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.
CRF_RNN16
50. online method using public detections
49.0
±0.0
53.918.1% 35.8% 8,49583,838621 (11.5)1,252 (23.2)Public
Anonymous submission
TrackerMOTAIDF1MTMLFPFNID Sw.FragDetector
RFS
51. online method using public detections
49.0
±0.0
50.315.3% 43.0% 9,95382,394701 (12.8)1,663 (30.3)Public
Anonymous submission
DAST
52. online method using public detections
48.9
±0.0
53.215.2% 36.2% 9,98782,427838 (15.3)1,936 (35.3)Public
Anonymous submission
KCF16
53. online method using public detections
48.8
±0.0
47.215.8% 38.1% 5,87586,567906 (17.3)1,116 (21.2)Public
P. Chu, H. Fan, C. Tan, H. Ling. Online Multi-Object Tracking with Instance-Aware Tracker and Dynamic Model Refreshment. In WACV, 2019.
LMP
54. online method using public detections
48.8
±0.0
51.318.2% 40.1% 6,65486,245481 (9.1)595 (11.3)Public
S. Tang, M. Andriluka, B. Andres, B. Schiele. Multiple People Tracking with Lifted Multicut and Person Re-identification. In CVPR, 2017.
DeepMP16
55. using public detections
48.7
±0.0
50.115.0% 43.6% 4,11188,862535 (10.4)873 (17.0)Public
Anonymous submission
TLMHT
56. online method using public detections
48.7
±0.0
55.315.7% 44.5% 6,63286,504413 (7.9)642 (12.2)Public
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.
STRN_MOT16
57. using public detections
48.5
±0.0
53.917.0% 34.9% 9,03884,178747 (13.9)2,919 (54.2)Public
J. Xu, Y. Cao, Z. Zhang, H. Hu. Spatial-Temporal Relation Networks for Multi-Object Tracking. In ICCV, 2019.
MOTPPF
58. using public detections
48.4
±0.0
48.519.1% 39.8% 9,15284,266595 (11.1)802 (14.9)Public
Anonymous submission
MOTPP
59. online method using public detections
48.3
±0.0
45.418.6% 40.1% 7,37886,181661 (12.5)834 (15.8)Public
Anonymous submission
AOReid
60. using public detections
48.2
±0.0
50.815.3% 36.8% 10,28383,301821 (15.1)1,963 (36.1)Public
Anonymous submission
TrackerMOTAIDF1MTMLFPFNID Sw.FragDetector
GCRA
61. online method using public detections
48.2
±0.0
48.612.9% 41.1% 5,10488,586821 (16.0)1,117 (21.7)Public
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.
SRPN16
62. online method using public detections
48.2
±0.0
51.314.2% 36.8% 7,76785,973790 (14.9)2,006 (38.0)Public
Anonymous submission
FWT
63. using public detections
47.8
±0.0
44.319.1% 38.2% 8,88685,487852 (16.0)1,534 (28.9)Public
R. Henschel, L. Leal-Taixé, D. Cremers, B. Rosenhahn. Fusion of Head and Full-Body Detectors for Multi-Object Tracking. In Trajnet CVPRW, 2018.
MOTDT
64. using public detections
47.6
±0.0
50.915.2% 38.3% 9,25385,431792 (14.9)1,858 (35.0)Public
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.
NLLMPa
65. using public detections
47.6
±0.0
47.317.0% 40.4% 5,84489,093629 (12.3)768 (15.0)Public
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.
WOLF
66. online method using public detections
47.5
±0.0
41.914.8% 35.7% 10,93983,4281,276 (23.5)2,480 (45.7)Public
Anonymous submission
CoCT16
67. using public detections
47.5
±0.0
53.219.4% 38.7% 16,97178,044682 (11.9)1,302 (22.8)Public
Anonymous submission
EAGS16
68. using public detections
47.4
±0.0
50.117.3% 42.7% 8,36986,931575 (11.0)913 (17.5)Public
H. Sheng, X. Zhang, Y. Zhang, Y. Wu, J. Chen. Enhanced Association with Supervoxels in Multiple Hypothesis Tracking. In IEEE Access, 2018.
JCSTD
69. using public detections
47.4
±0.0
41.114.4% 36.4% 8,07686,6381,266 (24.1)2,697 (51.4)Public
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.
ASTT
70. using public detections
47.2
±0.0
44.316.3% 41.6% 4,68090,877633 (12.6)814 (16.2)Public
Yi Tao el al., “Adaptive Spatio-temporal Model Based Multiple Object Tracking Considering a Moving Camera[C]”, International Conference on Universal Village (UV), 2018.
TrackerMOTAIDF1MTMLFPFNID Sw.FragDetector
eHAF16
71. online method
47.2
±0.0
52.418.6% 42.8% 12,58683,107542 (10.0)787 (14.5)Public
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.
AMIR
72. online method using public detections
47.2
±0.0
46.314.0% 41.6% 2,68192,856774 (15.8)1,675 (34.1)Public
A. Sadeghian, A. Alahi, S. Savarese. Tracking The Untrackable: Learning To Track Multiple Cues with Long-Term Dependencies. In ICCV, 2017.
MCjoint
73. online method using public detections
47.1
±0.0
52.320.4% 46.9% 6,70389,368370 (7.3)598 (11.7)Public
}@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} }
YOONKJ16
74. online method using public detections
47.0
±0.0
50.116.5% 41.8% 7,90188,179627 (12.1)945 (18.3)Public
Anonymous submission
NOMT
75. online method using public detections
46.4
±0.0
53.318.3% 41.4% 9,75387,565359 (6.9)504 (9.7)Public
W. Choi. Near-Online Multi-target Tracking with Aggregated Local Flow Descriptor. In ICCV, 2015.
JMC
76. online method
46.3
±0.0
46.315.5% 39.7% 6,37390,914657 (13.1)1,114 (22.2)Public
S. Tang, B. Andres, M. Andriluka, B. Schiele. Multi-Person Tracking by Multicuts and Deep Matching. In BMTT, 2016.
DD_TAMA16
77. online method using public detections
46.2
±0.0
49.414.1% 44.0% 5,12692,367598 (12.1)1,127 (22.8)Public
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
78. online method using public detections
46.1
±0.0
54.817.4% 42.7% 7,90989,874532 (10.5)1,616 (31.9)Public
J. Zhu, H. Yang, N. Liu, M. Kim, W. Zhang, M. Yang. Online Multi-Object Tracking with Dual Matching Attention Networks. In ECCV, 2018.
STAM16
79. online method
46.0
±0.0
50.014.6% 43.6% 6,89591,117473 (9.5)1,422 (28.4)Public
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.
deepS2
80. using public detections
46.0
±0.0
46.515.5% 42.6% 5,12492,697693 (14.1)759 (15.4)Public
ID 32
TrackerMOTAIDF1MTMLFPFNID Sw.FragDetector
RAR16pub
81. using public detections
45.9
±0.0
48.813.2% 41.9% 6,87191,173648 (13.0)1,992 (39.8)Public
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.
MHT_DAM
82. online method using public detections
45.8
±0.0
46.116.2% 43.2% 6,41291,758590 (11.9)781 (15.7)Public
C. Kim, F. Li, A. Ciptadi, J. Rehg. Multiple Hypothesis Tracking Revisited. In ICCV, 2015.
MTDF
83. using public detections
45.7
±0.0
40.114.1% 36.4% 12,01884,9701,987 (37.2)3,377 (63.2)Public
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.
INTERA_MOT
84. online method using public detections
45.4
±0.0
47.718.1% 38.7% 13,40785,547600 (11.3)930 (17.5)Public
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.
EDMT
85. using public detections
45.3
±0.0
47.917.0% 39.9% 11,12287,890639 (12.3)946 (18.3)Public
J. Chen, H. Sheng, Y. Zhang, Z. Xiong. Enhancing Detection Model for Multiple Hypothesis Tracking. In BMTT-PETS CVPRw, 2017.
DCCRF16
86. online method
44.8
±0.0
39.714.1% 42.3% 5,61394,133968 (20.0)1,378 (28.5)Public
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.
TBSS
87. online method using public detections
44.6
±0.0
42.612.3% 43.9% 4,13696,128790 (16.7)1,419 (30.0)Public
X. Zhou, P. Jiang, Z. Wei, H. Dong, F. Wang. Online Multi-Object Tracking with Structural Invariance Constraint. In BMVC, 2018.
OTCD_1
88. using public detections
44.4
±0.0
45.611.6% 47.6% 5,75994,927759 (15.8)1,787 (37.3)Public
Q. Liu, B. Liu, Y. Wu, W. Li, N. Yu. Real-Time Online Multi-Object Tracking in Compressed Domain. In IEEE Access, 2019.
QuadMOT16
89.
44.1
±0.0
38.314.6% 44.9% 6,38894,775745 (15.5)1,096 (22.8)Public
J. Son, M. Baek, M. Cho, B. Han. Multi-Object Tracking with Quadruplet Convolutional Neural Networks. In CVPR, 2017.
SRPN
90. using public detections
44.0
±0.0
36.615.5% 45.7% 18,78482,3181,047 (19.1)1,118 (20.4)Public
Anonymous submission
TrackerMOTAIDF1MTMLFPFNID Sw.FragDetector
CDA_DDALv2
91. online method using public detections
43.9
±0.0
45.110.7% 44.4% 6,45095,175676 (14.1)1,795 (37.6)Public
S. Bae and K. Yoon, Confidence-Based Data Association and Discriminative Deep Appearance Learning for Robust Online Multi-Object Tracking , In IEEE TPAMI, 2017.
LFNF16
92. using public detections
43.6
±0.0
41.613.3% 45.7% 6,61695,363836 (17.5)938 (19.7)Public
Sheng H, Hao L, Chen J, et al. Robust Local Effective Matching Model for Multi-Target Tracking. In PCM, 2017
oICF
93. online method using public detections
43.2
±0.0
49.311.3% 48.5% 6,65196,515381 (8.1)1,404 (29.8)Public
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.
AEb
94. using public detections
42.9
±0.0
48.715.3% 49.0% 4,48799,310375 (8.2)1,334 (29.3)Public
Anonymous submission
MHT_bLSTM6
95. using public detections
42.1
±0.0
47.814.9% 44.4% 11,63793,172753 (15.4)1,156 (23.6)Public
C. Kim, F. Li, J. Rehg. Multi-object Tracking with Neural Gating Using Bilinear LSTM. In ECCV, 2018.
TestUnsup
96. using public detections
41.5
±0.0
44.913.7% 43.5% 12,59693,404643 (13.2)796 (16.3)Public
Multi Object Tracking using Deep Structural Cost Minimization in Data Association
OST16
97.
41.5
±0.0
39.110.7% 45.6% 5,91999,7091,056 (23.3)1,487 (32.8)Public
Anonymous submission
LINF1
98. using public detections
41.0
±0.0
45.711.6% 51.3% 7,89699,224430 (9.4)963 (21.1)Public
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.
PHD_GSDL16
99. using public detections
41.0
±0.0
43.111.3% 41.5% 6,49899,2571,810 (39.7)3,650 (80.1)Public
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.
GMPHD_ReId
100.
40.4
±0.0
49.711.2% 43.3% 6,572101,266792 (17.8)2,529 (56.9)Public
N. Baisa. Occlusion-robust Online Multi-object Visual Tracking using a GM-PHD Filter with a CNN-based Re-identification. In , 2019.
TrackerMOTAIDF1MTMLFPFNID Sw.FragDetector
PHD_T
101. online method
40.4
±0.0
49.711.2% 43.3% 6,572101,266792 (17.8)2,529 (56.9)Public
Anonymous submission
PMPTracker
102. online method
40.3
±0.0
38.210.4% 42.0% 10,07197,5241,343 (28.9)2,764 (59.4)Public
Light version of PTZ camera Mutiple People Tracker
AM_ADM
103. using public detections
40.1
±0.0
43.87.1% 46.2% 8,50399,891789 (17.5)1,736 (38.4)Public
S. Lee, M. Kim, S. Bae, Learning Discriminative Appearance Models for Online Multi-Object Tracking with Appearance Discriminability Measures, In IEEE Access, 2018.
D_cost16
104. online method using public detections
39.9
±0.0
35.38.7% 50.2% 1,133107,586790 (19.3)824 (20.1)Public
Anonymous submission
SDMT
105. online method using public detections
39.6
±0.0
42.311.7% 49.1% 11,13098,343602 (13.1)772 (16.8)Public
M. Thoreau, N. Kottege. Deep Similarity Metric Learning for Real-Time Pedestrian Tracking. In arXiv, 2018.
EAMTT_pub
106. online method using public detections
38.8
±0.0
42.47.9% 49.1% 8,114102,452965 (22.0)1,657 (37.8)Public
R. Sanchez-Matilla, F. Poiesi, A. Cavallaro "Multi-target tracking with strong and weak detections" in BMTT ECCVw 2016
OVBT
107. online method using public detections
38.4
±0.0
37.87.5% 47.3% 11,51799,4631,321 (29.1)2,140 (47.1)Public
Y. Ban, S. Ba, X. Alameda-Pineda, R. Horaud. Tracking Multiple Persons Based on a Variational Bayesian Model. In BMTT 2016, .
FTPLS
108. using public detections
38.2
±0.0
47.59.6% 44.0% 18,91593,051689 (14.1)2,006 (41.0)Public
Anonymous submission
HAM_ACT16
109. using public detections
38.1
±0.0
43.37.8% 54.4% 6,976105,434418 (9.9)707 (16.8)Public
GMMCP
110. online method using public detections
38.1
±0.0
35.58.6% 50.9% 6,607105,315937 (22.2)1,669 (39.5)Public
A. Dehghan, S. Assari, M. Shah.. GMMCP-Tracker:Globally Optimal Generalized Maximum Multi Clique Problem for Multiple Object Tracking. In CVPR, 2015.
TrackerMOTAIDF1MTMLFPFNID Sw.FragDetector
LTTSC-CRF
111. online method using public detections
37.6
±0.0
42.19.6% 55.2% 11,969101,343481 (10.8)1,012 (22.8)Public
N. Le, A. Heili, M. Odobez. Long-Term Time-Sensitive Costs for CRF-Based Tracking by Detection. In ECCVw, 2016.
GoturnM16
112. online method
37.5
±0.0
25.18.4% 46.5% 17,74692,8673,277 (66.8)2,994 (61.0)Public
Anonymous submission
HISP_DAL
113. using public detections
37.4
±0.0
30.57.6% 50.9% 3,222108,8652,101 (52.1)2,151 (53.4)Public
N. Baisa. Robust Online Multi-target Visual Tracking using a HISP Filter with Discriminative Deep Appearance Learning. In CoRR, 2019.
JCmin_MOT
114. online method using public detections
36.7
±0.0
36.27.5% 54.4% 2,936111,890667 (17.3)831 (21.5)Public
M. Abhijeet Boragule. Joint Cost Minimization for Multi-Object Tracking. In 2017 IEEE International Conference on Advanced Vide and Signale Based Surveillance, 2017.
SCTrack_3L
115. online method
36.6
±0.0
36.529.0% 15.8% 35,25976,6533,600 (62.1)3,724 (64.3)Public
Anonymous submission
HISP_T
116. using public detections
35.9
±0.0
28.97.8% 50.1% 6,412107,9182,594 (63.6)2,298 (56.3)Public
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.
LP2D
117. using public detections
35.7
±0.0
34.28.7% 50.7% 5,084111,163915 (23.4)1,264 (32.4)Public
MOT baseline: Linear programming on 2D image coordinates.
GM_PHD_DAL
118. using public detections
35.1
±0.0
26.67.0% 51.4% 2,350111,8864,047 (104.8)5,338 (138.2)Public
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
119. using public detections
34.3
±0.0
20.57.1% 51.5% 2,350111,8865,605 (145.1)5,357 (138.7)Public
Anonymous submission
RNN_A_P
120. online method using public detections
34.0
±0.0
33.77.9% 51.0% 8,562109,2692,479 (61.9)3,393 (84.7)Public
Anonymous submission
TrackerMOTAIDF1MTMLFPFNID Sw.FragDetector
GM_PHD_e17
121. using public detections
33.8
±0.0
25.36.3% 54.9% 1,766115,1303,778 (102.5)3,874 (105.1)Public
Anonymous submission
TBD
122. using public detections
33.7
±0.0
0.07.2% 54.2% 5,804112,5872,418 (63.2)2,252 (58.9)Public
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.
KVIOU16
123. online method
33.4
±0.0
32.65.9% 59.6% 2,764117,971760 (21.5)1,473 (41.7)Public
Anonymous submission
GM_PHD_N1T
124. online method using public detections
33.3
±0.0
25.55.5% 56.0% 1,750116,4523,499 (96.8)3,594 (99.5)Public
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.
CEM
125. using public detections
33.2
±0.0
0.07.8% 54.4% 6,837114,322642 (17.2)731 (19.6)Public
A. Milan, S. Roth, K. Schindler. Continuous Energy Minimization for Multitarget Tracking. In IEEE TPAMI, 2014.
CppSORT
126. online method
31.5
±0.0
27.74.3% 59.9% 3,048120,2781,587 (46.6)2,239 (65.8)Public
S. Murray. Real-Time Multiple Object Tracking - A Study on the Importance of Speed. In arXiv preprint arXiv:1709.03572, 2017.
GMPHD_HDA
127. using public detections
30.5
±0.0
33.44.6% 59.7% 5,169120,970539 (16.0)731 (21.7)Public
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.
SMOT
128. online method using public detections
29.7
±0.0
0.05.3% 47.7% 17,426107,5523,108 (75.8)4,483 (109.3)Public
C. Dicle, O. Camps, M. Sznaier. The Way They Move: Tracking Targets with Similar Appearance. In ICCV, 2013.
DCOR
129. online method using public detections
28.3
±0.0
21.73.4% 63.9% 1,618128,345849 (28.7)2,592 (87.5)Public
Anonymous submission
MHT_ReID
130. online method using public detections
27.1
±0.0
36.430.6% 31.4% 13,068118,8291,071 (30.8)1,141 (32.8)Public
Anonymous submission
TrackerMOTAIDF1MTMLFPFNID Sw.FragDetector
JPDA_m
131. online method
26.2
±0.0
0.04.1% 67.5% 3,689130,549365 (12.9)638 (22.5)Public
H. Rezatofighi, A. Milan, Z. Zhang, Q. Shi, A. Dick, I. Reid. Joint Probabilistic Data Association Revisited. In ICCV, 2015.
DP_NMS
132. online method using public detections
26.2
±0.0
31.24.1% 67.5% 3,689130,557365 (12.9)638 (22.5)Public
H. Pirsiavash, D. Ramanan, C. Fowlkes. Globally-Optimal Greedy Algorithms for Tracking a Variable Number of Objects. In CVPR, 2011.
test_trker
133. using public detections
0.0
±0.0
0.00.0% 100.0% 7182,3260 (nan)0 (nan)Public
Anonymous submission
SequencesFramesTrajectoriesBoxes
75919759182326

Difficulty Analysis

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

MOT16-01

MOT16-01

(0.0% MOTA)

MOT16-03

MOT16-03

(0.0% MOTA)

MOT16-06

MOT16-06

(0.0% MOTA)

...

...

MOT16-12

MOT16-12

(0.0% MOTA)

MOT16-14

MOT16-14

(0.0% 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.