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
GM_PHD_Dl
1. 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
GM_PHD_DAL
2. 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_e17
3. 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
SCTrack_3L
4. online method
36.6
±0.0
36.529.0% 15.8% 35,25976,6533,600 (62.1)3,724 (64.3)Public
Anonymous submission
GM_PHD_N1T
5. 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.
GoturnM16
6. online method
37.5
±0.0
25.18.4% 46.5% 17,74692,8673,277 (66.8)2,994 (61.0)Public
Anonymous submission
SMOT
7. 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.
HISP_T
8. 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.
RNN_A_P
9. 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
TBD
10. 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.
TrackerMOTAIDF1MTMLFPFNID Sw.FragDetector
HISP_DAL
11. 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.
DpTrack
12. 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
MTDF
13. 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.
PHD_GSDL16
14. 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.
FFT16
15.
56.5
±0.0
50.123.6% 29.4% 5,83171,8251,635 (27.0)1,607 (26.5)Public
Anonymous submission
TrkReid
16. 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
CppSORT
17. 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.
TCT5
18. 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
MHT___ReID
19.
56.4
±0.0
54.239.7% 17.4% 23,79154,1691,478 (21.0)1,547 (22.0)Public
Anonymous submission
MTT_TPR
20. 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
TrackerMOTAIDF1MTMLFPFNID Sw.FragDetector
PMPTracker
21. 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
22. 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, .
WOLF
23. 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
JCSTD
24. 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.
MHT_ReID
25. 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
PV
26. online method
50.4
±0.0
50.814.9% 38.9% 2,60086,7801,061 (20.2)3,181 (60.7)Public
Anonymous submission
OST16
27.
41.5
±0.0
39.110.7% 45.6% 5,91999,7091,056 (23.3)1,487 (32.8)Public
Anonymous submission
SRPN
28. 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
DCCRF16
29. 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.
EAMTT_pub
30. 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
TrackerMOTAIDF1MTMLFPFNID Sw.FragDetector
RTT
31. 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
GMMCP
32. 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.
LP2D
33. 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.
Response
34. using public detections
62.0
±0.0
63.837.7% 20.7% 18,30850,039909 (12.5)2,009 (27.7)Public
Anonymous submission
KCF16
35. 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.
AFN
36. 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.
OMHT16
37. using public detections
49.8
±0.0
46.716.1% 40.4% 6,24484,342888 (16.5)1,332 (24.8)Public
Anonymous submission
DS_v2
38.
59.3
±0.0
57.524.2% 29.1% 7,46565,810887 (13.9)2,738 (42.8)Public
Anonymous submission
SCNet
39. using public detections
50.0
±0.0
51.115.5% 34.1% 10,52679,755866 (15.4)2,141 (38.1)Public
Anonymous submission
FWT
40. 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.
TrackerMOTAIDF1MTMLFPFNID Sw.FragDetector
DCOR
41. 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
DAST
42. 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
LFNF16
43. 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
MMHT16
44. using public detections
49.9
±0.0
47.316.2% 40.7% 6,11084,455823 (15.3)1,289 (24.0)Public
Anonymous submission
AOReid
45. using public detections
48.2
±0.0
50.815.3% 36.8% 10,28383,301821 (15.1)1,963 (36.1)Public
Anonymous submission
GCRA
46. 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.
retrack
47. 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
TTL16
48. using public detections
50.4
±0.0
50.117.4% 39.9% 8,49181,156807 (14.5)1,251 (22.5)Public
Anonymous submission
GMPHD_ReId
49.
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.
MOTDT
50. 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.
TrackerMOTAIDF1MTMLFPFNID Sw.FragDetector
PHD_T
51. online method
40.4
±0.0
49.711.2% 43.3% 6,572101,266792 (17.8)2,529 (56.9)Public
Anonymous submission
D_cost16
52. 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
SRPN16
53. 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
TBSS
54. 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.
AM_ADM
55. 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.
TLO
56. 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
TLO16
57. using public detections
49.8
±0.0
47.816.6% 40.6% 6,08584,623782 (14.6)1,278 (23.8)Public
Anonymous submission
AMIR
58. 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.
moT_
59. 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
KVIOU16
60. online method
33.4
±0.0
32.65.9% 59.6% 2,764117,971760 (21.5)1,473 (41.7)Public
Anonymous submission
TrackerMOTAIDF1MTMLFPFNID Sw.FragDetector
OTCD_1
61. 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.
MHT_bLSTM6
62. 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.
STRN_MOT16
63. 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.
QuadMOT16
64.
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.
dpt_dpt
65. using public detections
61.3
±0.0
60.432.1% 18.6% 12,41157,481739 (10.8)1,960 (28.6)Public
Anonymous submission
ISE_MOT16
66. using public detections
60.1
±0.0
56.926.1% 29.1% 6,96465,044739 (11.5)951 (14.8)Public
MIFT
UTA
67. 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
MEN
68. 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
CRFTrack16
69. 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
70. online method
50.3
±0.0
54.418.3% 35.7% 7,14882,746702 (12.9)1,387 (25.4)Public
Anonymous submission
TrackerMOTAIDF1MTMLFPFNID Sw.FragDetector
RFS
71. 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
deepS2
72. using public detections
46.0
±0.0
46.515.5% 42.6% 5,12492,697693 (14.1)759 (15.4)Public
ID 32
FTPLS
73. using public detections
38.2
±0.0
47.59.6% 44.0% 18,91593,051689 (14.1)2,006 (41.0)Public
Anonymous submission
ReTrack16
74. online method
57.0
±0.0
54.221.9% 34.3% 4,44673,258688 (11.5)1,543 (25.8)Public
Anonymous submission
CoCT16
75. using public detections
47.5
±0.0
53.219.4% 38.7% 16,97178,044682 (11.9)1,302 (22.8)Public
Anonymous submission
Tracktor16
76. 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.
MOTHP
77.
49.1
±0.0
46.920.0% 38.9% 9,03883,031679 (12.5)850 (15.6)Public
Anonymous submission
siameseCos
78.
49.4
±0.0
49.819.1% 39.4% 6,28185,384679 (12.8)823 (15.5)Public
In preparation
CDA_DDALv2
79. 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.
JCmin_MOT
80. 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.
TrackerMOTAIDF1MTMLFPFNID Sw.FragDetector
MOT_FILTER
81. using public detections
50.2
±0.0
46.817.9% 39.7% 5,26784,812664 (12.4)978 (18.3)Public
Anonymous submission
MOTPP
82. 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
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.
MOTHPCLEAN
84.
50.4
±0.0
47.019.1% 39.5% 5,33284,505657 (12.2)862 (16.1)Public
Anonymous submission
RAR16pub
85. 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.
TestUnsup
86. 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
CEM
87. 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.
EDMT
88. 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.
MOTPP16
89. online method
50.5
±0.0
47.219.6% 39.4% 5,93983,694638 (11.8)823 (15.2)Public
Anonymous submission
ASTT
90. 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
NLLMPa
91. 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.
pairwise16
92.
50.0
±0.0
52.419.4% 38.7% 10,99579,568628 (11.1)939 (16.7)Public
Anonymous submission
YOONKJ16
93. 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
CRF_RNN16
94. 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
MOTHYPER
95.
50.9
±0.0
47.419.4% 39.4% 4,86684,022619 (11.5)814 (15.1)Public
Anonymous submission
HDTR
96. online method using public detections
53.6
±0.0
46.621.2% 37.0% 4,71479,353618 (10.9)833 (14.7)Public
NOTA
97. 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.
eTC
98. 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.
LSST16O
99. 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
100. 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
SDMT
101. 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.
INTERA_MOT
102. 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.
DD_TAMA16
103. 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.
MOTPPF
104. using public detections
48.4
±0.0
48.519.1% 39.8% 9,15284,266595 (11.1)802 (14.9)Public
Anonymous submission
MHT_DAM
105. 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.
EAGS16
106. 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.
TPM
107. using public detections
51.3
±0.0
47.918.7% 40.8% 2,70185,504569 (10.7)707 (13.3)Public
Anonymous submission
eHAF16
108. 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.
GMPHD_HDA
109. 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.
DeepMP16
110. 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
DMAN
111. 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.
STCG
112. online method
49.3
±0.0
52.016.2% 41.4% 6,88684,979515 (9.6)775 (14.5)Public
Anonymous submission
ENFT16
113.
50.3
±0.0
55.019.2% 39.8% 8,34181,843490 (8.9)754 (13.7)Public
BUAA
HTBT16
114. 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
LMP
115. 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.
LTTSC-CRF
116. 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.
ENFT
117. online method
50.0
±0.0
54.617.8% 41.1% 8,21482,541479 (8.8)724 (13.2)Public
Anonymous submission
STAM16
118. 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.
MPNTrack16
119. 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
LINF1
120. 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.
TrackerMOTAIDF1MTMLFPFNID Sw.FragDetector
HAM_ACT16
121. using public detections
38.1
±0.0
43.37.8% 54.4% 6,976105,434418 (9.9)707 (16.8)Public
TLMHT
122. 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.
HCC
123. 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.
Lif_T
124. 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
oICF
125. 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
126. using public detections
42.9
±0.0
48.715.3% 49.0% 4,48799,310375 (8.2)1,334 (29.3)Public
Anonymous submission
MCjoint
127. 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} }
CMT16
128. 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
DP_NMS
129. 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.
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
NOMT
131. 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.
MLT
132. online method
52.8
±0.0
62.621.1% 42.4% 5,36280,444299 (5.4)702 (12.6)Public
Anonymous submission
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.