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
Lif_T
1. 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
Response
2. using public detections
62.0
±0.0
63.837.7% 20.7% 18,30850,039909 (12.5)2,009 (27.7)Public
Anonymous submission
MLT
3. online method
52.8
±0.0
62.621.1% 42.4% 5,36280,444299 (5.4)702 (12.6)Public
Anonymous submission
dpt_dpt
4. using public detections
61.3
±0.0
60.432.1% 18.6% 12,41157,481739 (10.8)1,960 (28.6)Public
Anonymous submission
MPNTrack16
5. 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
CMT16
6. 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
DS_v2
7.
59.3
±0.0
57.524.2% 29.1% 7,46565,810887 (13.9)2,738 (42.8)Public
Anonymous submission
ISE_MOT16
8. using public detections
60.1
±0.0
56.926.1% 29.1% 6,96465,044739 (11.5)951 (14.8)Public
MIFT
LSST16O
9. 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
SORT_Alex
10. 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
TrackerMOTAIDF1MTMLFPFNID Sw.FragDetector
eTC
11. 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.
NOTA
12. 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.
TLMHT
13. 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.
ENFT16
14.
50.3
±0.0
55.019.2% 39.8% 8,34181,843490 (8.9)754 (13.7)Public
BUAA
HTBT16
15. 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
DMAN
16. 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.
ENFT
17. online method
50.0
±0.0
54.617.8% 41.1% 8,21482,541479 (8.8)724 (13.2)Public
Anonymous submission
CRFTrack16
18. 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
19. online method
50.3
±0.0
54.418.3% 35.7% 7,14882,746702 (12.9)1,387 (25.4)Public
Anonymous submission
TrkReid
20. 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
TrackerMOTAIDF1MTMLFPFNID Sw.FragDetector
MHT___ReID
21.
56.4
±0.0
54.239.7% 17.4% 23,79154,1691,478 (21.0)1,547 (22.0)Public
Anonymous submission
ReTrack16
22. online method
57.0
±0.0
54.221.9% 34.3% 4,44673,258688 (11.5)1,543 (25.8)Public
Anonymous submission
CRF_RNN16
23. 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
STRN_MOT16
24. 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.
NOMT
25. 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.
CoCT16
26. using public detections
47.5
±0.0
53.219.4% 38.7% 16,97178,044682 (11.9)1,302 (22.8)Public
Anonymous submission
DAST
27. 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
MTT_TPR
28. 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
DpTrack
29. 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
MEN
30. 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
TrackerMOTAIDF1MTMLFPFNID Sw.FragDetector
retrack
31. 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_
32. 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
Tracktor16
33. 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.
eHAF16
34. 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.
pairwise16
35.
50.0
±0.0
52.419.4% 38.7% 10,99579,568628 (11.1)939 (16.7)Public
Anonymous submission
MCjoint
36. 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} }
STCG
37. online method
49.3
±0.0
52.016.2% 41.4% 6,88684,979515 (9.6)775 (14.5)Public
Anonymous submission
TCT5
38. 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
SRPN16
39. 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
LMP
40. 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.
TrackerMOTAIDF1MTMLFPFNID Sw.FragDetector
SCNet
41. using public detections
50.0
±0.0
51.115.5% 34.1% 10,52679,755866 (15.4)2,141 (38.1)Public
Anonymous submission
MOTDT
42. 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.
AOReid
43. using public detections
48.2
±0.0
50.815.3% 36.8% 10,28383,301821 (15.1)1,963 (36.1)Public
Anonymous submission
PV
44. online method
50.4
±0.0
50.814.9% 38.9% 2,60086,7801,061 (20.2)3,181 (60.7)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.
UTA
46. 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
RFS
47. 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
FFT16
48.
56.5
±0.0
50.123.6% 29.4% 5,83171,8251,635 (27.0)1,607 (26.5)Public
Anonymous submission
EAGS16
49. 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.
DeepMP16
50. using public detections
48.7
±0.0
50.115.0% 43.6% 4,11188,862535 (10.4)873 (17.0)Public
Anonymous submission
TrackerMOTAIDF1MTMLFPFNID Sw.FragDetector
TTL16
51. using public detections
50.4
±0.0
50.117.4% 39.9% 8,49181,156807 (14.5)1,251 (22.5)Public
Anonymous submission
YOONKJ16
52. 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
STAM16
53. 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.
siameseCos
54.
49.4
±0.0
49.819.1% 39.4% 6,28185,384679 (12.8)823 (15.5)Public
In preparation
GMPHD_ReId
55.
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.
PHD_T
56. online method
40.4
±0.0
49.711.2% 43.3% 6,572101,266792 (17.8)2,529 (56.9)Public
Anonymous submission
DD_TAMA16
57. 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.
oICF
58. 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.
RTT
59. 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
RAR16pub
60. 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.
TrackerMOTAIDF1MTMLFPFNID Sw.FragDetector
AEb
61. using public detections
42.9
±0.0
48.715.3% 49.0% 4,48799,310375 (8.2)1,334 (29.3)Public
Anonymous submission
GCRA
62. 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.
MOTPPF
63. using public detections
48.4
±0.0
48.519.1% 39.8% 9,15284,266595 (11.1)802 (14.9)Public
Anonymous submission
AFN
64. 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.
TLO
65. 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
TPM
66. using public detections
51.3
±0.0
47.918.7% 40.8% 2,70185,504569 (10.7)707 (13.3)Public
Anonymous submission
EDMT
67. 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.
MHT_bLSTM6
68. 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.
TLO16
69. using public detections
49.8
±0.0
47.816.6% 40.6% 6,08584,623782 (14.6)1,278 (23.8)Public
Anonymous submission
INTERA_MOT
70. 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.
TrackerMOTAIDF1MTMLFPFNID Sw.FragDetector
FTPLS
71. using public detections
38.2
±0.0
47.59.6% 44.0% 18,91593,051689 (14.1)2,006 (41.0)Public
Anonymous submission
MOTHYPER
72.
50.9
±0.0
47.419.4% 39.4% 4,86684,022619 (11.5)814 (15.1)Public
Anonymous submission
NLLMPa
73. 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.
MMHT16
74. using public detections
49.9
±0.0
47.316.2% 40.7% 6,11084,455823 (15.3)1,289 (24.0)Public
Anonymous submission
KCF16
75. 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.
MOTPP16
76. online method
50.5
±0.0
47.219.6% 39.4% 5,93983,694638 (11.8)823 (15.2)Public
Anonymous submission
MOTHPCLEAN
77.
50.4
±0.0
47.019.1% 39.5% 5,33284,505657 (12.2)862 (16.1)Public
Anonymous submission
MOTHP
78.
49.1
±0.0
46.920.0% 38.9% 9,03883,031679 (12.5)850 (15.6)Public
Anonymous submission
MOT_FILTER
79. using public detections
50.2
±0.0
46.817.9% 39.7% 5,26784,812664 (12.4)978 (18.3)Public
Anonymous submission
OMHT16
80. using public detections
49.8
±0.0
46.716.1% 40.4% 6,24484,342888 (16.5)1,332 (24.8)Public
Anonymous submission
TrackerMOTAIDF1MTMLFPFNID Sw.FragDetector
HDTR
81. online method using public detections
53.6
±0.0
46.621.2% 37.0% 4,71479,353618 (10.9)833 (14.7)Public
deepS2
82. using public detections
46.0
±0.0
46.515.5% 42.6% 5,12492,697693 (14.1)759 (15.4)Public
ID 32
JMC
83. 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.
AMIR
84. 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.
MHT_DAM
85. 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.
LINF1
86. 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.
OTCD_1
87. 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.
MOTPP
88. 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
CDA_DDALv2
89. 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.
TestUnsup
90. 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
TrackerMOTAIDF1MTMLFPFNID Sw.FragDetector
FWT
91. 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.
ASTT
92. 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.
AM_ADM
93. 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.
HAM_ACT16
94. using public detections
38.1
±0.0
43.37.8% 54.4% 6,976105,434418 (9.9)707 (16.8)Public
PHD_GSDL16
95. 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.
TBSS
96. 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.
EAMTT_pub
97. 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
SDMT
98. 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.
LTTSC-CRF
99. 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.
WOLF
100. 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
TrackerMOTAIDF1MTMLFPFNID Sw.FragDetector
LFNF16
101. 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
JCSTD
102. 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.
MTDF
103. 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.
DCCRF16
104. 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.
OST16
105.
41.5
±0.0
39.110.7% 45.6% 5,91999,7091,056 (23.3)1,487 (32.8)Public
Anonymous submission
QuadMOT16
106.
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.
PMPTracker
107. 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
OVBT
108. 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, .
SRPN
109. 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
SCTrack_3L
110. online method
36.6
±0.0
36.529.0% 15.8% 35,25976,6533,600 (62.1)3,724 (64.3)Public
Anonymous submission
TrackerMOTAIDF1MTMLFPFNID Sw.FragDetector
MHT_ReID
111. 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
JCmin_MOT
112. 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.
GMMCP
113. 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.
D_cost16
114. 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
LP2D
115. 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.
RNN_A_P
116. 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
GMPHD_HDA
117. 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.
KVIOU16
118. online method
33.4
±0.0
32.65.9% 59.6% 2,764117,971760 (21.5)1,473 (41.7)Public
Anonymous submission
DP_NMS
119. 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.
HISP_DAL
120. 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.
TrackerMOTAIDF1MTMLFPFNID Sw.FragDetector
HISP_T
121. 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.
CppSORT
122. 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.
GM_PHD_DAL
123. 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_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.
GM_PHD_e17
125. 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
GoturnM16
126. online method
37.5
±0.0
25.18.4% 46.5% 17,74692,8673,277 (66.8)2,994 (61.0)Public
Anonymous submission
DCOR
127. 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
GM_PHD_Dl
128. 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
CEM
129. 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.
JPDA_m
130. 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.
TrackerMOTAIDF1MTMLFPFNID Sw.FragDetector
SMOT
131. 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.
TBD
132. 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.
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.