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 RankMOTAIDF1MTMLFPFNID Sw.FragHzDetector
AFN
1. using public detections
34.1
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.
AMIR
2. online method using public detections
41.3
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.
AM_ADM
3. online method using public detections
52.8
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.
AOReid
4. online method using public detections
34.8
48.2
±8.7
50.815.3% 36.8% 10,28383,301821 (15.1)1,963 (36.1)11.2Public
Anonymous submission
ASTT
5. using public detections
36.3
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.
CDA_DDALv2
6. online method using public detections
49.8
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.
CEM
7. using public detections
55.7
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.
CMT16
8. using public detections
23.3
49.8
±9.0
59.216.6% 43.6% 9,22981,882365 (6.6)617 (11.2)6.3Public
#Submission: TIP-21190-2019
CppSORT
9. online method using public detections
55.9
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.
CRFTrack16
10. using public detections
27.3
50.3
±7.9
54.418.3% 35.7% 7,14882,746702 (12.9)1,387 (25.4)1.5Public
Anonymous submission
TrackerAvg RankMOTAIDF1MTMLFPFNID Sw.FragHzDetector
CRF_RNN16
11. using public detections
28.8
49.0
±7.2
53.918.1% 35.8% 8,49583,838621 (11.5)1,252 (23.2)1.5Public
Anonymous submission
CRF_TRACK
12. using public detections
26.7
50.3
±7.9
54.418.3% 35.7% 7,14882,746702 (12.9)1,387 (25.4)1.5Public
Anonymous submission
DAST
13. online method using public detections
33.4
48.9
±8.4
53.215.2% 36.2% 9,98782,427838 (15.3)1,936 (35.3)8.7Public
Anonymous submission
DCCRF16
14. online method using public detections
49.9
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.
DCOR
15. online method using public detections
56.0
28.3
±9.0
21.73.4% 63.9% 1,618128,345849 (28.7)2,592 (87.5)32.9Public
Anonymous submission
DD_TAMA16
16. online method using public detections
36.3
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.
DeepMP16
17. using public detections
29.3
48.7
±10.3
50.115.0% 43.6% 4,11188,862535 (10.4)873 (17.0)9.9Public
Anonymous submission
deepS2
18. using public detections
39.3
46.0
±8.2
46.515.5% 42.6% 5,12492,697693 (14.1)759 (15.4)0.7Public
ID 32
DMAN
19. online method using public detections
39.8
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.
DpTrack
20. using public detections
25.5
59.3
±18.7
52.827.4% 24.6% 8,56663,6032,045 (31.4)1,555 (23.9)10.4Public
Anonymous submission
TrackerAvg RankMOTAIDF1MTMLFPFNID Sw.FragHzDetector
DP_NMS
21. using public detections
47.9
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.
DS_v2
22. using public detections
21.9
59.3
±12.9
57.524.2% 29.1% 7,46565,810887 (13.9)2,738 (42.8)39.4Public
Anonymous submission
D_cost16
23. online method using public detections
46.0
39.9
±9.1
35.38.7% 50.2% 1,133107,586790 (19.3)824 (20.1)8.5Public
Anonymous submission
EAGS16
24. using public detections
31.2
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.
EAMTT_pub
25. online method using public detections
54.3
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
EDMT
26. using public detections
40.8
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.
eHAF16
27. using public detections
35.5
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.
ENFT
28. using public detections
21.3
50.0
±8.2
54.617.8% 41.1% 8,21482,541479 (8.8)724 (13.2)22.3Public
Anonymous submission
ENFT16
29. using public detections
25.5
50.3
±8.3
55.019.2% 39.8% 8,34181,843490 (8.9)754 (13.7)0.4Public
BUAA
eTC
30. using public detections
31.5
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 arXiv preprint arXiv:1811.07258, 2018.
TrackerAvg RankMOTAIDF1MTMLFPFNID Sw.FragHzDetector
FWT
31. using public detections
41.8
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.
GCRA
32. using public detections
39.2
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.
GMMCP
33. using public detections
59.8
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.
GMPHD_HDA
34. online method using public detections
48.9
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.
GM_PHD_DAL
35. online method using public detections
61.8
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
36. online method using public detections
62.8
34.3
±9.1
20.57.1% 51.5% 2,350111,8865,605 (145.1)5,357 (138.7)3.5Public
Anonymous submission
GM_PHD_e17
37. online method using public detections
63.3
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_N1T
38. online method using public detections
61.2
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.
HAM_ACT16
39. online method using public detections
46.5
38.1
±8.2
43.37.8% 54.4% 6,976105,434418 (9.9)707 (16.8)8.0Public
HCC
40. using public detections
27.6
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.
TrackerAvg RankMOTAIDF1MTMLFPFNID Sw.FragHzDetector
HDTR
41. using public detections
22.3
53.6
±8.7
46.621.2% 37.0% 4,71479,353618 (10.9)833 (14.7)3.6Public
HISP_DAL
42. online method using public detections
59.2
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.
HISP_T
43. online method using public detections
61.3
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.
INTERA_MOT
44. using public detections
37.8
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.
JCmin_MOT
45. online method using public detections
48.7
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.
JCSTD
46. online method using public detections
46.1
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.
JMC
47. using public detections
41.3
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.
JPDA_m
48. using public detections
49.0
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.
KCF16
49. online method using public detections
41.6
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.
LFNF16
50. using public detections
52.5
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
TrackerAvg RankMOTAIDF1MTMLFPFNID Sw.FragHzDetector
LINF1
51. using public detections
48.1
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.
LMP
52. using public detections
31.6
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.
LP2D
53. using public detections
52.3
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.
LSST16O
54. online method using public detections
38.4
49.2
±10.2
56.513.4% 41.4% 7,18784,875606 (11.3)2,497 (46.7)2.0Public
Anonymous submission
LTTSC-CRF
55. using public detections
57.7
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.
MCjoint
56. using public detections
34.1
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} }
MEN
57. online method using public detections
34.9
50.0
±9.1
52.815.0% 37.0% 6,11784,271706 (13.1)1,797 (33.4)2.0Public
Anonymous submission
MHT_bLSTM6
58. using public detections
52.0
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.
MHT_DAM
59. using public detections
41.8
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.
MHT_ReID
60. using public detections
54.8
27.1
±47.2
36.430.6% 31.4% 13,068118,8291,071 (30.8)1,141 (32.8)0.5Public
Anonymous submission
TrackerAvg RankMOTAIDF1MTMLFPFNID Sw.FragHzDetector
MHT___ReID
61. using public detections
35.0
56.4
±11.6
54.239.7% 17.4% 23,79154,1691,478 (21.0)1,547 (22.0)0.5Public
Anonymous submission
MOTDT
62. online method using public detections
39.8
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.
MOTHP
63. using public detections
31.9
49.1
±9.1
46.920.0% 38.9% 9,03883,031679 (12.5)850 (15.6)11.8Public
Anonymous submission
MOTHPCLEAN
64. using public detections
27.4
50.4
±9.4
47.019.1% 39.5% 5,33284,505657 (12.2)862 (16.1)11.8Public
Anonymous submission
MOTPP
65. using public detections
35.0
48.3
±8.7
45.418.6% 40.1% 7,37886,181661 (12.5)834 (15.8)11.8Public
Anonymous submission
MOTPP16
66. using public detections
28.8
50.5
±9.7
47.219.6% 39.4% 5,93983,694638 (11.8)823 (15.2)3.0Public
Anonymous submission
MOTPPF
67. using public detections
31.3
48.4
±8.8
48.519.1% 39.8% 9,15284,266595 (11.1)802 (14.9)11.8Public
Anonymous submission
MOT_FILTER
68. using public detections
30.7
50.2
±12.9
46.817.9% 39.7% 5,26784,812664 (12.4)978 (18.3)11.8Public
Anonymous submission
MTDF
69. online method using public detections
54.9
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.
MTT_TPR
70. using public detections
30.4
54.9
±11.7
53.118.7% 34.8% 4,13076,6731,447 (25.0)3,693 (63.7)6.7Public
Anonymous submission
TrackerAvg RankMOTAIDF1MTMLFPFNID Sw.FragHzDetector
NLLMPa
71. using public detections
34.8
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.
NOMT
72. using public detections
33.5
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.
NOTA
73. using public detections
27.8
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.
oICF
74. online method using public detections
50.8
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.
OST16
75. online method using public detections
55.6
41.5
±9.2
39.110.7% 45.6% 5,91999,7091,056 (23.3)1,487 (32.8)4.7Public
Anonymous submission
OVBT
76. online method using public detections
68.8
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, .
pairwise16
77. using public detections
27.6
50.0
±65.9
52.419.4% 38.7% 10,99579,568628 (11.1)939 (16.7)22.3Public
Anonymous submission
PHD_GSDL16
78. online method using public detections
56.4
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.
PHD_T
79. online method using public detections new
61.8
37.1
±8.7
39.410.1% 45.7% 6,679104,8943,095 (72.9)3,671 (86.4)9.9Public
Anonymous submission
PMPTracker
80. online method using public detections
58.4
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 RankMOTAIDF1MTMLFPFNID Sw.FragHzDetector
PV
81. online method using public detections
39.0
50.4
±10.1
50.814.9% 38.9% 2,60086,7801,061 (20.2)3,181 (60.7)7.3Public
Anonymous submission
QuadMOT16
82. using public detections
51.7
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.
RAR16pub
83. online method using public detections
50.4
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.
retrack
84. online method using public detections
52.1
26.2
±13.2
31.017.4% 26.6% 52,61078,8453,042 (53.6)4,835 (85.2)22.3Public
Anonymous submission
ReTrack16
85. using public detections
27.2
57.0
±12.3
54.221.9% 34.3% 4,44673,258688 (11.5)1,543 (25.8)0.8Public
Anonymous submission
RNN_A_P
86. online method using public detections
66.2
34.0
±8.6
33.77.9% 51.0% 8,562109,2692,479 (61.9)3,393 (84.7)19.7Public
Anonymous submission
RTT
87. online method using public detections
40.6
49.9
±8.0
49.319.0% 32.8% 9,92780,406955 (17.1)2,247 (40.2)1.8Public
Anonymous submission
SCNet
88. online method using public detections
45.9
48.8
±8.8
51.515.4% 33.5% 11,66680,725891 (16.0)2,100 (37.7)0.3Public
Anonymous submission
siameseCos
89. using public detections
34.3
49.4
±8.4
49.819.1% 39.4% 6,28185,384679 (12.8)823 (15.5)0.8Public
In preparation
SMOT
90. using public detections
78.1
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.
TrackerAvg RankMOTAIDF1MTMLFPFNID Sw.FragHzDetector
SRPN16
91. online method using public detections
45.6
48.2
±8.5
51.314.2% 36.8% 7,76785,973790 (14.9)2,006 (38.0)1.4Public
Anonymous submission
STAM16
92. online method using public detections
49.1
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.
STCG
93. using public detections
30.3
49.3
±8.6
52.016.2% 41.4% 6,88684,979515 (9.6)775 (14.5)22.3Public
Anonymous submission
TBD
94. using public detections
69.8
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.
TBSS
95. online method using public detections
52.2
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.
TestUnsup
96. online method using public detections
49.5
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
test_trker
97. using public detections
51.7
0.0
±0.0
0.00.0% 100.0% 7182,3260 (nan)0 (nan)22.3Public
Anonymous submission
TLMHT
98. using public detections
33.9
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.
TPM
99. using public detections
30.9
51.3
±9.3
47.918.7% 40.8% 2,70185,504569 (10.7)707 (13.3)0.8Public
Anonymous submission
Tracktor16
100. online method using public detections
29.9
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.
TrackerAvg RankMOTAIDF1MTMLFPFNID Sw.FragHzDetector
UTA
101. online method using public detections
35.7
50.6
±7.9
50.418.3% 33.5% 7,75281,584722 (13.1)2,196 (39.7)5.0Public
Anonymous submission
YOONKJ16
102. online method using public detections
43.3
47.0
±8.4
50.116.5% 41.8% 7,90188,179627 (12.1)945 (18.3)3.5Public
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

(51.9% MOTA)

MOT16-06

MOT16-06

(45.0% MOTA)

MOT16-07

MOT16-07

(38.9% MOTA)

...

...

MOT16-08

MOT16-08

(30.0% MOTA)

MOT16-14

MOT16-14

(24.8% 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.