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
test_trker
1. using public detections
0.0
±0.0
0.00.0% 100.0% 7182,3260 (nan)0 (nan)Public
Anonymous submission
DP_NMS
2. 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
3. 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.
DCOR
4. 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
GMPHD_HDA
5. 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.
CppSORT
6. 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.
MHT_ReID
7. 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
KVIOU16
8. online method
33.4
±0.0
32.65.9% 59.6% 2,764117,971760 (21.5)1,473 (41.7)Public
Anonymous submission
GM_PHD_N1T
9. 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
10. 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
TrackerMOTAIDF1MTMLFPFNID Sw.FragDetector
CEM
11. 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.
TBD
12. 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.
JCmin_MOT
13. 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.
GM_PHD_DAL
14. using public detections
35.1
±0.0
26.67.0% 51.4% 2,350111,8864,047 (104.8)5,338 (138.2)Public
N. Baisa. Online Multi-object Visual Tracking using a GM-PHD Filter with Deep Appearance Learning. In 22nd International Conference on Information Fusion, 2019.
GM_PHD_Dl
15. 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
LP2D
16. 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
17. 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
HISP_DAL
18. 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.
HISP_T
19. 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.
D_cost16
20. 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
TrackerMOTAIDF1MTMLFPFNID Sw.FragDetector
SMOT
21. 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.
HAM_ACT16
22. using public detections
38.1
±0.0
43.37.8% 54.4% 6,976105,434418 (9.9)707 (16.8)Public
GMMCP
23. 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.
EAMTT_pub
24. 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
LTTSC-CRF
25. 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.
GMPHD_ReId
26.
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
27. online method
40.4
±0.0
49.711.2% 43.3% 6,572101,266792 (17.8)2,529 (56.9)Public
Anonymous submission
AM_ADM
28. 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.
OST16
29.
41.5
±0.0
39.110.7% 45.6% 5,91999,7091,056 (23.3)1,487 (32.8)Public
Anonymous submission
OVBT
30. 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, .
TrackerMOTAIDF1MTMLFPFNID Sw.FragDetector
AEb
31. using public detections
42.9
±0.0
48.715.3% 49.0% 4,48799,310375 (8.2)1,334 (29.3)Public
Anonymous submission
PHD_GSDL16
32. 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.
LINF1
33. 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.
SDMT
34. 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.
PMPTracker
35. 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
oICF
36. 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.
TBSS
37. 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.
LFNF16
38. 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
CDA_DDALv2
39. 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.
OTCD_1
40. 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.
TrackerMOTAIDF1MTMLFPFNID Sw.FragDetector
QuadMOT16
41.
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.
DCCRF16
42. 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.
TestUnsup
43. 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
MHT_bLSTM6
44. 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.
FTPLS
45. using public detections
38.2
±0.0
47.59.6% 44.0% 18,91593,051689 (14.1)2,006 (41.0)Public
Anonymous submission
GoturnM16
46. online method
37.5
±0.0
25.18.4% 46.5% 17,74692,8673,277 (66.8)2,994 (61.0)Public
Anonymous submission
AMIR
47. 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.
deepS2
48. using public detections
46.0
±0.0
46.515.5% 42.6% 5,12492,697693 (14.1)759 (15.4)Public
ID 32
DD_TAMA16
49. 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.
MHT_DAM
50. 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.
TrackerMOTAIDF1MTMLFPFNID Sw.FragDetector
RAR16pub
51. 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.
STAM16
52. 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.
JMC
53. 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.
ASTT
54. 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.
DMAN
55. 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.
MCjoint
56. 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} }
NLLMPa
57. 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.
DeepMP16
58. using public detections
48.7
±0.0
50.115.0% 43.6% 4,11188,862535 (10.4)873 (17.0)Public
Anonymous submission
GCRA
59. 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.
YOONKJ16
60. 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
TrackerMOTAIDF1MTMLFPFNID Sw.FragDetector
EDMT
61. 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.
NOMT
62. 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.
EAGS16
63. 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.
HCC
64. 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.
PV
65. online method
50.4
±0.0
50.814.9% 38.9% 2,60086,7801,061 (20.2)3,181 (60.7)Public
Anonymous submission
JCSTD
66. 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.
KCF16
67. 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.
TLMHT
68. 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.
LMP
69. 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.
MOTPP
70. 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
TrackerMOTAIDF1MTMLFPFNID Sw.FragDetector
SRPN16
71. 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
INTERA_MOT
72. 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.
TPM
73. using public detections
51.3
±0.0
47.918.7% 40.8% 2,70185,504569 (10.7)707 (13.3)Public
Anonymous submission
FWT
74. using public detections
47.8
±0.0
44.319.1% 38.2% 8,88685,487852 (16.0)1,534 (28.9)Public
R. Henschel, L. Leal-Taixé, D. Cremers, B. Rosenhahn. Fusion of Head and Full-Body Detectors for Multi-Object Tracking. In Trajnet CVPRW, 2018.
MOTDT
75. 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.
siameseCos
76.
49.4
±0.0
49.819.1% 39.4% 6,28185,384679 (12.8)823 (15.5)Public
In preparation
STCG
77. online method
49.3
±0.0
52.016.2% 41.4% 6,88684,979515 (9.6)775 (14.5)Public
Anonymous submission
MTDF
78. 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.
LSST16O
79. 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
MOT_FILTER
80. using public detections
50.2
±0.0
46.817.9% 39.7% 5,26784,812664 (12.4)978 (18.3)Public
Anonymous submission
TrackerMOTAIDF1MTMLFPFNID Sw.FragDetector
TLO
81. 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
82. using public detections
49.8
±0.0
47.816.6% 40.6% 6,08584,623782 (14.6)1,278 (23.8)Public
Anonymous submission
MOTHPCLEAN
83.
50.4
±0.0
47.019.1% 39.5% 5,33284,505657 (12.2)862 (16.1)Public
Anonymous submission
MMHT16
84. using public detections
49.9
±0.0
47.316.2% 40.7% 6,11084,455823 (15.3)1,289 (24.0)Public
Anonymous submission
OMHT16
85. using public detections
49.8
±0.0
46.716.1% 40.4% 6,24484,342888 (16.5)1,332 (24.8)Public
Anonymous submission
MEN
86. 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
MOTPPF
87. using public detections
48.4
±0.0
48.519.1% 39.8% 9,15284,266595 (11.1)802 (14.9)Public
Anonymous submission
STRN_MOT16
88. 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.
MOTHYPER
89.
50.9
±0.0
47.419.4% 39.4% 4,86684,022619 (11.5)814 (15.1)Public
Anonymous submission
CRF_RNN16
90. online method using public detections
49.0
±0.0
53.918.1% 35.8% 8,49583,838621 (11.5)1,252 (23.2)Public
Anonymous submission
TrackerMOTAIDF1MTMLFPFNID Sw.FragDetector
eTC
91. 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.
MOTPP16
92. online method
50.5
±0.0
47.219.6% 39.4% 5,93983,694638 (11.8)823 (15.2)Public
Anonymous submission
NOTA
93. 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.
WOLF
94. 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
AOReid
95. using public detections
48.2
±0.0
50.815.3% 36.8% 10,28383,301821 (15.1)1,963 (36.1)Public
Anonymous submission
eHAF16
96. 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.
MOTHP
97.
49.1
±0.0
46.920.0% 38.9% 9,03883,031679 (12.5)850 (15.6)Public
Anonymous submission
CRFTrack16
98. 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
99. online method
50.3
±0.0
54.418.3% 35.7% 7,14882,746702 (12.9)1,387 (25.4)Public
Anonymous submission
ENFT
100. online method
50.0
±0.0
54.617.8% 41.1% 8,21482,541479 (8.8)724 (13.2)Public
Anonymous submission
TrackerMOTAIDF1MTMLFPFNID Sw.FragDetector
AFN
101. 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.
DAST
102. 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
RFS
103. 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
SRPN
104. 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
CMT16
105. 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
ENFT16
106.
50.3
±0.0
55.019.2% 39.8% 8,34181,843490 (8.9)754 (13.7)Public
BUAA
HTBT16
107. 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
UTA
108. 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
TTL16
109. using public detections
50.4
±0.0
50.117.4% 39.9% 8,49181,156807 (14.5)1,251 (22.5)Public
Anonymous submission
MLT
110. online method
52.8
±0.0
62.621.1% 42.4% 5,36280,444299 (5.4)702 (12.6)Public
Anonymous submission
TrackerMOTAIDF1MTMLFPFNID Sw.FragDetector
RTT
111. 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
SCNet
112. using public detections
50.0
±0.0
51.115.5% 34.1% 10,52679,755866 (15.4)2,141 (38.1)Public
Anonymous submission
pairwise16
113.
50.0
±0.0
52.419.4% 38.7% 10,99579,568628 (11.1)939 (16.7)Public
Anonymous submission
HDTR
114. online method using public detections
53.6
±0.0
46.621.2% 37.0% 4,71479,353618 (10.9)833 (14.7)Public
Tracktor16
115. 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.
CoCT16
116. using public detections
47.5
±0.0
53.219.4% 38.7% 16,97178,044682 (11.9)1,302 (22.8)Public
Anonymous submission
moT_
117. 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
MTT_TPR
118. 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
SCTrack_3L
119. online method
36.6
±0.0
36.529.0% 15.8% 35,25976,6533,600 (62.1)3,724 (64.3)Public
Anonymous submission
retrack
120. 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
TrackerMOTAIDF1MTMLFPFNID Sw.FragDetector
ReTrack16
121. online method
57.0
±0.0
54.221.9% 34.3% 4,44673,258688 (11.5)1,543 (25.8)Public
Anonymous submission
MPNTrack16
122. 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
SORT_Alex
123. 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
FFT16
124.
56.5
±0.0
50.123.6% 29.4% 5,83171,8251,635 (27.0)1,607 (26.5)Public
Anonymous submission
TCT5
125. 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
DS_v2
126.
59.3
±0.0
57.524.2% 29.1% 7,46565,810887 (13.9)2,738 (42.8)Public
Anonymous submission
Lif_T
127. 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
ISE_MOT16
128. using public detections
60.1
±0.0
56.926.1% 29.1% 6,96465,044739 (11.5)951 (14.8)Public
MIFT
DpTrack
129. 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
TrkReid
130. 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
dpt_dpt
131. using public detections
61.3
±0.0
60.432.1% 18.6% 12,41157,481739 (10.8)1,960 (28.6)Public
Anonymous submission
MHT___ReID
132.
56.4
±0.0
54.239.7% 17.4% 23,79154,1691,478 (21.0)1,547 (22.0)Public
Anonymous submission
Response
133. using public detections
62.0
±0.0
63.837.7% 20.7% 18,30850,039909 (12.5)2,009 (27.7)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.