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
CMT16
1. using public detections
24.9
49.8
±9.0
59.216.6% 43.6% 9,22981,882365 (6.6)617 (11.2)6.3Public
#Submission: TIP-21190-2019
DS_v2
2. using public detections
23.4
59.3
±12.9
57.524.2% 29.1% 7,46565,810887 (13.9)2,738 (42.8)39.4Public
Anonymous submission
LSST16O
3. online method using public detections
41.4
49.2
±10.2
56.513.4% 41.4% 7,18784,875606 (11.3)2,497 (46.7)2.0Public
Anonymous submission
eTC
4. using public detections
32.9
49.2
±9.1
56.117.3% 40.3% 8,40083,702606 (11.2)882 (16.3)0.7Public
G. Wang, Y. Wang, H. Zhang, R. Gu, J. Hwang. Exploit the connectivity: Multi-object tracking with trackletnet. In Proceedings of the 27th ACM International Conference on Multimedia, 2019.
NOTA
5. using public detections
29.3
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.
TLMHT
6. using public detections
36.7
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.
ENFT16
7. using public detections
26.4
50.3
±8.3
55.019.2% 39.8% 8,34181,843490 (8.9)754 (13.7)0.4Public
BUAA
DMAN
8. online method using public detections
42.3
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.
ENFT
9. using public detections
22.3
50.0
±8.2
54.617.8% 41.1% 8,21482,541479 (8.8)724 (13.2)22.3Public
Anonymous submission
CRF_TRACK
10. using public detections
27.9
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
CRFTrack16
11. using public detections
28.6
50.3
±7.9
54.418.3% 35.7% 7,14882,746702 (12.9)1,387 (25.4)1.5Public
Anonymous submission
MHT___ReID
12. using public detections
37.3
56.4
±11.6
54.239.7% 17.4% 23,79154,1691,478 (21.0)1,547 (22.0)0.5Public
Anonymous submission
ReTrack16
13. using public detections
28.4
57.0
±12.3
54.221.9% 34.3% 4,44673,258688 (11.5)1,543 (25.8)0.8Public
Anonymous submission
CRF_RNN16
14. using public detections
30.2
49.0
±7.2
53.918.1% 35.8% 8,49583,838621 (11.5)1,252 (23.2)1.5Public
Anonymous submission
STRN_MOT16
15. using public detections
41.0
48.5
±8.5
53.917.0% 34.9% 9,03884,178747 (13.9)2,919 (54.2)13.5Public
J. Xu, Y. Cao, Z. Zhang, H. Hu. Spatial-Temporal Relation Networks for Multi-Object Tracking. In ICCV, 2019.
NOMT
16. using public detections
35.8
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.
DAST
17. online method using public detections
36.2
48.9
±8.4
53.215.2% 36.2% 9,98782,427838 (15.3)1,936 (35.3)8.7Public
Anonymous submission
MTT_TPR
18. using public detections
32.4
54.9
±11.7
53.118.7% 34.8% 4,13076,6731,447 (25.0)3,693 (63.7)6.7Public
Anonymous submission
DpTrack
19. using public detections
27.6
59.3
±18.7
52.827.4% 24.6% 8,56663,6032,045 (31.4)1,555 (23.9)10.4Public
Anonymous submission
MEN
20. online method using public detections
36.8
50.0
±9.1
52.815.0% 37.0% 6,11784,271706 (13.1)1,797 (33.4)2.0Public
Anonymous submission
TrackerAvg RankMOTAIDF1MTMLFPFNID Sw.FragHzDetector
Tracktor16
21. online method using public detections
31.6
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.
eHAF16
22. using public detections
37.4
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.
pairwise16
23. using public detections
28.9
50.0
±65.9
52.419.4% 38.7% 10,99579,568628 (11.1)939 (16.7)22.3Public
Anonymous submission
MCjoint
24. using public detections
36.2
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} }
STCG
25. using public detections
32.3
49.3
±8.6
52.016.2% 41.4% 6,88684,979515 (9.6)775 (14.5)22.3Public
Anonymous submission
SRPN16
26. online method using public detections
48.8
48.2
±8.5
51.314.2% 36.8% 7,76785,973790 (14.9)2,006 (38.0)1.4Public
Anonymous submission
LMP
27. using public detections
33.3
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.
SCNet
28. online method using public detections
47.1
50.0
±8.9
51.115.5% 34.1% 10,52679,755866 (15.4)2,141 (38.1)0.3Public
Anonymous submission
MOTDT
29. online method using public detections
42.5
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.
AOReid
30. online method using public detections
37.4
48.2
±8.7
50.815.3% 36.8% 10,28383,301821 (15.1)1,963 (36.1)11.2Public
Anonymous submission
TrackerAvg RankMOTAIDF1MTMLFPFNID Sw.FragHzDetector
PV
31. online method using public detections
41.9
50.4
±10.1
50.814.9% 38.9% 2,60086,7801,061 (20.2)3,181 (60.7)7.3Public
Anonymous submission
HCC
32. using public detections
29.0
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.
UTA
33. online method using public detections
37.6
50.6
±7.9
50.418.3% 33.5% 7,75281,584722 (13.1)2,196 (39.7)5.0Public
Anonymous submission
EAGS16
34. using public detections
32.8
47.4
±10.4
50.117.3% 42.7% 8,36986,931575 (11.0)913 (17.5)197.3Public
H. Sheng, X. Zhang, Y. Zhang, Y. Wu, J. Chen. Enhanced Association with Supervoxels in Multiple Hypothesis Tracking. In IEEE Access, 2018.
DeepMP16
35. using public detections
31.5
48.7
±10.3
50.115.0% 43.6% 4,11188,862535 (10.4)873 (17.0)9.9Public
Anonymous submission
YOONKJ16
36. online method using public detections
46.1
47.0
±8.4
50.116.5% 41.8% 7,90188,179627 (12.1)945 (18.3)3.5Public
Anonymous submission
STAM16
37. online method using public detections
52.2
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.
siameseCos
38. using public detections
36.5
49.4
±8.4
49.819.1% 39.4% 6,28185,384679 (12.8)823 (15.5)0.8Public
In preparation
DD_TAMA16
39. online method using public detections
38.6
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.
oICF
40. online method using public detections
53.8
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.
TrackerAvg RankMOTAIDF1MTMLFPFNID Sw.FragHzDetector
RTT
41. online method using public detections
43.2
49.9
±8.0
49.319.0% 32.8% 9,92780,406955 (17.1)2,247 (40.2)1.8Public
Anonymous submission
RAR16pub
42. online method using public detections
53.5
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.
GCRA
43. using public detections
41.8
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.
MOTPPF
44. using public detections
33.2
48.4
±8.8
48.519.1% 39.8% 9,15284,266595 (11.1)802 (14.9)11.8Public
Anonymous submission
PHD_T
45. online method using public detections
57.3
40.3
±9.0
48.311.6% 43.1% 7,147100,895815 (18.2)2,446 (54.8)9.9Public
Anonymous submission
AFN
46. using public detections
36.5
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.
TLO16
47. online method using public detections new
38.8
50.1
±9.9
48.016.3% 40.7% 5,61584,626788 (14.7)1,296 (24.2)12.4Public
Anonymous submission
TPM
48. using public detections
32.8
51.3
±9.3
47.918.7% 40.8% 2,70185,504569 (10.7)707 (13.3)0.8Public
Anonymous submission
EDMT
49. using public detections
43.0
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.
MHT_bLSTM6
50. using public detections
55.3
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.
TrackerAvg RankMOTAIDF1MTMLFPFNID Sw.FragHzDetector
INTERA_MOT
51. using public detections
40.0
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.
NLLMPa
52. using public detections
36.7
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.
MMHT16
53. online method using public detections
37.3
49.9
±9.8
47.316.2% 40.7% 6,11084,455823 (15.3)1,289 (24.0)12.4Public
Anonymous submission
KCF16
54. online method using public detections
44.3
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.
MOTPP16
55. using public detections
30.1
50.5
±9.7
47.219.6% 39.4% 5,93983,694638 (11.8)823 (15.2)3.0Public
Anonymous submission
MOTHPCLEAN
56. using public detections
28.9
50.4
±9.4
47.019.1% 39.5% 5,33284,505657 (12.2)862 (16.1)11.8Public
Anonymous submission
MOTHP
57. using public detections
33.9
49.1
±9.1
46.920.0% 38.9% 9,03883,031679 (12.5)850 (15.6)11.8Public
Anonymous submission
MOT_FILTER
58. using public detections
32.3
50.2
±12.9
46.817.9% 39.7% 5,26784,812664 (12.4)978 (18.3)11.8Public
Anonymous submission
OMHT16
59. online method using public detections new
40.0
49.8
±9.9
46.716.1% 40.4% 6,24484,342888 (16.5)1,332 (24.8)12.4Public
Anonymous submission
HDTR
60. using public detections
23.4
53.6
±8.7
46.621.2% 37.0% 4,71479,353618 (10.9)833 (14.7)3.6Public
TrackerAvg RankMOTAIDF1MTMLFPFNID Sw.FragHzDetector
deepS2
61. using public detections
41.8
46.0
±8.2
46.515.5% 42.6% 5,12492,697693 (14.1)759 (15.4)0.7Public
ID 32
JMC
62. using public detections
43.8
46.3
±9.0
46.315.5% 39.7% 6,37390,914657 (13.1)1,114 (22.2)0.8Public
S. Tang, B. Andres, M. Andriluka, B. Schiele. Multi-Person Tracking by Multicuts and Deep Matching. In BMTT, 2016.
AMIR
63. online method using public detections
44.3
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.
retrack
64. online method using public detections
41.8
49.2
±14.2
46.116.5% 36.1% 6,49585,214915 (17.2)2,078 (39.0)22.3Public
Anonymous submission
MHT_DAM
65. using public detections
44.6
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.
LINF1
66. using public detections
51.3
41.0
±9.5
45.711.6% 51.3% 7,89699,224430 (9.4)963 (21.1)4.2Public
L. Fagot-Bouquet, R. Audigier, Y. Dhome, F. Lerasle. Improving Multi-Frame Data Association with Sparse Representations for Robust Near-Online Multi-Object Tracking. In ECCV, 2016.
MOTPP
67. using public detections
37.6
48.3
±8.7
45.418.6% 40.1% 7,37886,181661 (12.5)834 (15.8)11.8Public
Anonymous submission
CDA_DDALv2
68. online method using public detections
53.3
43.9
±7.8
45.110.7% 44.4% 6,45095,175676 (14.1)1,795 (37.6)0.5Public
S. Bae and K. Yoon, Confidence-Based Data Association and Discriminative Deep Appearance Learning for Robust Online Multi-Object Tracking , In IEEE TPAMI, 2017.
TestUnsup
69. online method using public detections
52.8
41.5
±9.0
44.913.7% 43.5% 12,59693,404643 (13.2)796 (16.3)19.7Public
Multi Object Tracking using Deep Structural Cost Minimization in Data Association
FWT
70. using public detections
45.0
47.8
±9.4
44.319.1% 38.2% 8,88685,487852 (16.0)1,534 (28.9)0.6Public
R. Henschel, L. Leal-Taixé, D. Cremers, B. Rosenhahn. Fusion of Head and Full-Body Detectors for Multi-Object Tracking. In Trajnet CVPRW, 2018.
TrackerAvg RankMOTAIDF1MTMLFPFNID Sw.FragHzDetector
ASTT
71. using public detections
38.8
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.
AM_ADM
72. online method using public detections
56.6
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.
HAM_ACT16
73. online method using public detections
49.7
38.1
±8.2
43.37.8% 54.4% 6,976105,434418 (9.9)707 (16.8)8.0Public
PHD_GSDL16
74. online method using public detections
61.1
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.
TBSS
75. online method using public detections
55.8
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.
EAMTT_pub
76. online method using public detections
58.6
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
SDMT
77. online method using public detections
54.0
39.6
±8.3
42.311.7% 49.1% 11,13098,343602 (13.1)772 (16.8)19.8Public
M. Thoreau, N. Kottege. Deep Similarity Metric Learning for Real-Time Pedestrian Tracking. In arXiv, 2018.
LTTSC-CRF
78. using public detections
61.4
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.
LFNF16
79. using public detections
56.2
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
JCSTD
80. online method using public detections
50.0
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.
TrackerAvg RankMOTAIDF1MTMLFPFNID Sw.FragHzDetector
MTDF
81. online method using public detections
59.2
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.
DCCRF16
82. online method using public detections
53.5
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.
OST16
83. online method using public detections
59.8
41.5
±9.2
39.110.7% 45.6% 5,91999,7091,056 (23.3)1,487 (32.8)4.7Public
Anonymous submission
QuadMOT16
84. using public detections
54.9
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.
PMPTracker
85. online method using public detections
62.6
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
OVBT
86. online method using public detections
73.6
38.4
±8.8
37.87.5% 47.3% 11,51799,4631,321 (29.1)2,140 (47.1)0.3Public
Y. Ban, S. Ba, X. Alameda-Pineda, R. Horaud. Tracking Multiple Persons Based on a Variational Bayesian Model. In BMTT 2016, .
MHT_ReID
87. using public detections
58.2
27.1
±47.2
36.430.6% 31.4% 13,068118,8291,071 (30.8)1,141 (32.8)0.5Public
Anonymous submission
JCmin_MOT
88. online method using public detections
51.3
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.
GMMCP
89. using public detections
64.1
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.
D_cost16
90. online method using public detections
49.2
39.9
±9.1
35.38.7% 50.2% 1,133107,586790 (19.3)824 (20.1)8.5Public
Anonymous submission
TrackerAvg RankMOTAIDF1MTMLFPFNID Sw.FragHzDetector
LP2D
91. using public detections
55.3
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.
RNN_A_P
92. online method using public detections
70.6
34.0
±8.6
33.77.9% 51.0% 8,562109,2692,479 (61.9)3,393 (84.7)19.7Public
Anonymous submission
GMPHD_HDA
93. online method using public detections
51.8
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.
KVIOU16
94. using public detections new
57.3
33.4
±9.7
32.65.9% 59.6% 2,764117,971760 (21.5)1,473 (41.7)29.6Public
Anonymous submission
DP_NMS
95. using public detections
51.1
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.
HISP_DAL
96. online method using public detections
63.3
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
97. online method using public detections
65.5
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.
CppSORT
98. online method using public detections
59.8
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.
GM_PHD_DAL
99. online method using public detections
65.6
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_N1T
100. online method using public detections
65.3
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.
TrackerAvg RankMOTAIDF1MTMLFPFNID Sw.FragHzDetector
GM_PHD_e17
101. online method using public detections
67.1
33.8
±8.9
25.36.3% 54.9% 1,766115,1303,778 (102.5)3,874 (105.1)3.3Public
Anonymous submission
DCOR
102. online method using public detections
59.7
28.3
±9.0
21.73.4% 63.9% 1,618128,345849 (28.7)2,592 (87.5)32.9Public
Anonymous submission
GM_PHD_Dl
103. online method using public detections
66.5
34.3
±9.1
20.57.1% 51.5% 2,350111,8865,605 (145.1)5,357 (138.7)3.5Public
Anonymous submission
SMOT
104. using public detections
82.9
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.
JPDA_m
105. using public detections
51.7
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.
CEM
106. using public detections
58.9
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.
TBD
107. using public detections
74.3
33.7
±9.2
0.07.2% 54.2% 5,804112,5872,418 (63.2)2,252 (58.9)1.3Public
A. Geiger, M. Lauer, C. Wojek, C. Stiller, R. Urtasun. 3D Traffic Scene Understanding from Movable Platforms. In Pattern Analysis and Machine Intelligence (PAMI), 2014.
test_trker
108. using public detections
54.7
0.0
±0.0
0.00.0% 100.0% 7182,3260 (nan)0 (nan)22.3Public
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

(52.5% MOTA)

MOT16-06

MOT16-06

(45.4% MOTA)

MOT16-07

MOT16-07

(39.5% MOTA)

...

...

MOT16-08

MOT16-08

(30.3% MOTA)

MOT16-14

MOT16-14

(25.2% 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.