2D MOT 2015 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 RankMOTAIDF1MT MLFPFNID Sw.FragHzDetector
HOHOTRACK
1. online method using public detections
37.9
26.8
±21.7
32.928.6% 16.9% 18,99424,5491,411 (23.5)3,417 (56.9)26.7Public
Anonymous submission
MR
2. using public detections
29.8
36.6
±16.6
47.233.1% 21.5% 16,69621,428850 (13.1)1,156 (17.8)0.3Public
Anonymous submission
MPNTrack15
3. using public detections
18.8
48.3
±12.0
56.532.2% 24.3% 9,64021,629504 (7.8)1,074 (16.6)9.3Public
Anonymous submission
MHTREID15
4. using public detections
26.3
40.0
±16.2
49.429.7% 24.4% 12,78023,378684 (11.0)1,112 (17.9)0.5Public
Anonymous submission
Tracktor15
5. online method using public detections
32.7
44.1
±11.7
46.718.0% 26.2% 6,47726,5771,318 (23.2)1,790 (31.5)0.9Public
P. Bergmann, T. Meinhardt, L. Leal-Taixé. Tracking without bells and whistles. In ICCV, 2019.
AMIR15
6. online method using public detections
28.4
37.6
±12.5
46.015.8% 26.8% 7,93329,3971,026 (19.7)2,024 (38.8)1.9Public
A. Sadeghian, A. Alahi, S. Savarese. Tracking The Untrackable: Learning To Track Multiple Cues with Long-Term Dependencies. In ICCV, 2017.
CRFTrack_
7. using public detections
23.1
40.0
±14.5
49.623.0% 28.6% 10,29525,917658 (11.4)1,508 (26.1)3.2Public
Anonymous submission
TLO15
8. online method using public detections
32.8
40.0
±14.9
44.317.1% 28.8% 9,34926,3281,207 (21.1)1,624 (28.4)12.1Public
Anonymous submission
CRF_RNN15
9. using public detections
22.2
38.9
±15.1
49.320.9% 29.4% 10,66926,291591 (10.3)1,270 (22.2)3.2Public
Anonymous submission
INARLA
10. online method using public detections
36.4
34.7
±13.2
42.112.5% 30.0% 9,85529,1581,112 (21.2)2,848 (54.2)2.6Public
H. Wu, Y. Hu, K. Wang, H. Li, L. Nie, H. Cheng. Instance-aware representation learning and association for online multi-person tracking. In Pattern Recognition, 2019.
TrackerAvg RankMOTAIDF1MT MLFPFNID Sw.FragHzDetector
SMOTe
11. online method using public detections
41.8
28.0
±16.1
45.415.0% 30.8% 15,88127,372977 (17.6)2,106 (38.0)1.6Public
Anonymous submission
KCF
12. online method using public detections
27.9
38.9
±14.5
44.516.6% 31.5% 7,32129,501720 (13.9)1,440 (27.7)0.3Public
P. Chu, H. Fan, C. Tan, H. Ling. Online Multi-Object Tracking with Instance-Aware Tracker and Dynamic Model Refreshment. In WACV, 2019.
LDCT
13. online method using public detections
50.6
4.7
±41.3
16.811.4% 32.5% 14,06632,15612,348 (259.1)2,918 (61.2)20.7Public
F. Solera, S. Calderara, R. Cucchiara. Learning to Divide and Conquer for Online Multi-Target Tracking. In ICCV, 2015
STRN
14. online method using public detections
31.3
38.1
±11.3
46.611.5% 33.4% 5,45131,5711,033 (21.2)2,665 (54.8)13.8Public
J. Xu, Y. Cao, Z. Zhang, H. Hu. Spatial-Temporal Relation Networks for Multi-Object Tracking. In ICCV, 2019.
SORT_Y
15. online method using public detections
52.1
11.8
±18.5
26.110.0% 33.4% 19,80331,4762,893 (59.3)3,801 (77.9)334.8Public
Anonymous submission
TLO
16. using public detections
30.3
41.3
±13.7
46.115.7% 34.5% 8,00027,210852 (15.3)1,405 (25.2)5.0Public
Anonymous submission
DeepMP
17. using public detections
21.5
40.5
±12.8
28.816.8% 35.2% 6,27929,654599 (11.6)1,034 (20.0)9.6Public
Anonymous submission
SNM
18. online method using public detections
38.6
31.3
±16.5
38.212.6% 35.4% 8,90332,393926 (19.6)2,382 (50.4)14.8Public
Anonymous submission
RSCNN
19. using public detections
37.8
29.5
±23.9
37.012.9% 36.3% 11,86630,474976 (19.4)1,176 (23.3)4.0Public
Heba Mahgoub, Khaled Mostafa, Khaled T. Wassif and Ibrahim Farag, “Multi-Target Tracking Using Hierarchical Convolutional Features and Motion Cues” International Journal of Advanced Computer Science and Applications(IJACSA), 8(11), 2017.
QuadMOT
20. using public detections
32.9
33.8
±14.8
40.412.9% 36.9% 7,89832,061703 (14.7)1,430 (29.9)3.7Public
J. Son, M. Baek, M. Cho, B. Han. Multi-Object Tracking with Quadruplet Convolutional Neural Networks. In CVPR, 2017.
TrackerAvg RankMOTAIDF1MT MLFPFNID Sw.FragHzDetector
MTSTracker
21. online method using public detections
49.3
20.6
±18.2
31.99.0% 36.9% 15,16132,2121,387 (29.2)2,357 (49.5)19.3Public
F. Nguyen Thi Lan Anh, F. Bremond. Multi-Object Tracking using Multi-Channel Part Appearance Representation. In International conference on Advanced video and Signal Based Surveillance, 2017.
AP_HWDPL_p
22. online method using public detections
21.1
38.5
±9.9
47.18.7% 37.4% 4,00533,203586 (12.8)1,263 (27.5)6.7Public
C. Long, A. Haizhou, S. Chong, Z. Zijie, B. Bo. Online Multi-Object Tracking with Convolutional Neural Networks. In 2017 IEEE International Conference on Image Processing (ICIP), 2017.
DCCRF
23. online method using public detections
33.6
33.6
±11.0
39.110.4% 37.6% 5,91734,002866 (19.4)1,566 (35.1)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.
MMHT15
24. online method using public detections
38.3
29.8
±17.0
38.012.1% 38.0% 10,54831,3901,189 (24.3)1,612 (33.0)12.1Public
Anonymous submission
GMPHD_OGM
25. online method using public detections
29.4
30.7
±12.6
38.811.5% 38.1% 6,50235,0301,034 (24.1)1,351 (31.4)169.5Public
Y. Song, K. Yoon, Y. Yoon, K. Yow, M. Jeon. Online Multi-Object Tracking with GMPHD Filter and Occlusion Group Management. In IEEE Access, 2019.
mLK
26. online method using public detections
25.5
35.1
±12.9
47.512.3% 38.3% 5,67833,815383 (8.5)1,175 (26.1)1.0Public
Yuan Zhang, Di Xie and Shiliang Pu (Hikvision Research Institute)
MDP
27. online method using public detections
35.7
30.3
±14.6
44.713.0% 38.4% 9,71732,422680 (14.4)1,500 (31.8)1.1Public
Y. Xiang, A. Alahi, S. Savarese. Learning to Track: Online Multi-Object Tracking by Decision Making. In International Conference on Computer Vision (ICCV), 2015.
TDAM
28. online method using public detections
32.5
33.0
±9.8
46.113.3% 39.1% 10,06430,617464 (9.2)1,506 (30.0)5.9Public
M. Yang, Y. Jia. Temporal dynamic appearance modeling for online multi-person tracking. In Computer Vision and Image Understanding, 2016.
JointMC
29. using public detections
26.5
35.6
±18.9
45.123.2% 39.3% 10,58028,508457 (8.5)969 (18.1)0.6Public
M. Keuper, S. Tang, B. Andres, T. Brox, B. Schiele. Motion Segmentation amp; Multiple Object Tracking by Correlation Co-Clustering. In IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018.
TSMLCDEnew
30. using public detections
29.9
34.3
±13.1
44.114.0% 39.4% 7,86931,908618 (12.9)959 (20.0)6.5Public
B. Wang, G. Wang, K. L. Chan, L. Wang. Tracklet Association by Online Target-Specific Metric Learning and Coherent Dynamics Estimation. In arXiv:1511.06654, 2015.
TrackerAvg RankMOTAIDF1MT MLFPFNID Sw.FragHzDetector
EDA_GNN
31. online method using public detections
46.3
21.8
±13.8
27.89.0% 40.2% 11,97034,5871,488 (34.0)1,851 (42.4)56.4Public
Paper ID 2713
DP_NMS
32. using public detections
52.2
14.5
±14.5
19.76.0% 40.8% 13,17134,8144,537 (104.7)3,090 (71.3)444.8Public
H. Pirsiavash, D. Ramanan, C. Fowlkes. Globally-Optimal Greedy Algorithms for Tracking a Variable Number of Objects. In CVPR, 2011.
CF_MCMC
33. using public detections
36.6
31.4
±11.3
36.410.3% 40.9% 8,79832,541814 (17.3)1,711 (36.4)3.2Public
Anonymous submission
LP2D
34. using public detections
52.1
19.8
±14.2
0.06.7% 41.2% 11,58036,0451,649 (39.9)1,712 (41.4)112.1Public
MOT baseline: Linear programming on 2D image coordinates.
PHD_GSDL
35. online method using public detections
41.2
30.5
±14.9
38.87.6% 41.2% 6,53435,284879 (20.6)2,208 (51.9)8.2Public
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.
LFNF
36. using public detections
35.8
31.6
±12.3
33.19.6% 41.7% 5,94335,095961 (22.4)1,106 (25.8)4.0Public
Sheng H, Hao L, Chen J, et al. Robust Local Effective Matching Model for Multi-Target Tracking. In PCM, 2017
SRPN
37. online method using public detections
43.3
31.0
±13.3
30.712.6% 41.7% 10,24131,0991,062 (21.5)1,370 (27.7)3.9Public
Anonymous submission
HybridDAT
38. online method using public detections
26.0
35.0
±15.0
47.711.4% 42.2% 8,45531,140358 (7.3)1,267 (25.7)4.6Public
M. Yang, Y. Jia. A Hybrid Data Association Framework for Robust Online Multi-Object Tracking. In IEEE Transactions on Image Processing, 2016.
CDA_DDALpb
39. online method using public detections
31.5
32.8
±10.6
38.89.7% 42.2% 4,98335,690614 (14.7)1,583 (37.8)2.3Public
S. Bae and K. Yoon, Confidence-Based Data Association and Discriminative Deep Appearance Learning for Robust Online Multi-Object Tracking , In IEEE TPAMI, 2017.
TSDA_OAL
40. online method using public detections
48.4
18.6
±17.6
36.19.4% 42.3% 16,35032,853806 (17.3)1,544 (33.2)19.7Public
H. Ko. Online multi-person tracking with two-stage data association and online appearance model learning. In IET Computer Vision, 2017.
TrackerAvg RankMOTAIDF1MT MLFPFNID Sw.FragHzDetector
RAR15pub
41. online method using public detections
28.9
35.1
±12.5
45.413.0% 42.3% 6,77132,717381 (8.1)1,523 (32.6)5.4Public
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.
MHT__ReID
42. using public detections
28.8
33.0
±15.1
46.417.6% 42.6% 8,72532,046421 (8.8)851 (17.8)0.3Public
Anonymous submission
GMMA_intp
43. online method using public detections
40.3
27.3
±12.0
36.66.5% 43.1% 7,84835,817987 (23.7)1,848 (44.3)132.5Public
Y. Song, Y. Yoon, K. Yoon, M. Jeon. Online and Real-Time Tracking with the GMPHD Filter using Group Management and Relative Motion Analysis. In Proc. IEEE Int. Workshop Traffic Street Surveill. Safety Secur. (AVSS), 2018.
siam
44. online method using public detections
38.2
33.0
±17.0
36.28.9% 43.3% 5,10135,190853 (20.0)1,078 (25.2)1.9Public
Anonymous submission
AM
45. online method using public detections
25.4
34.3
±13.7
48.311.4% 43.4% 5,15434,848348 (8.0)1,463 (33.8)0.5Public
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.
dSRPN15
46. online method using public detections
36.3
33.3
±15.3
32.79.3% 43.7% 7,82532,211919 (19.3)1,276 (26.8)3.9Public
Anonymous submission
ELP
47. using public detections
48.6
25.0
±10.8
26.27.5% 43.8% 7,34537,3441,396 (35.6)1,804 (46.0)5.7Public
N. McLaughlin, J. Martinez Del Rincon, P. Miller. Enhancing Linear Programming with Motion Modeling for Multi-target Tracking. In IEEE Winter Conference on Applications of Computer Vision (WACV), 2015.
MHT_DAM
48. using public detections
30.0
32.4
±15.6
45.316.0% 43.8% 9,06432,060435 (9.1)826 (17.3)0.7Public
C. Kim, F. Li, A. Ciptadi, J. Rehg. Multiple Hypothesis Tracking Revisited. In ICCV, 2015.
TBSS15
49. online method using public detections
41.1
29.2
±12.5
37.26.8% 43.8% 6,06836,779649 (16.2)1,508 (37.6)11.5Public
X. Zhou, P. Jiang, Z. Wei, H. Dong, F. Wang. Online Multi-Object Tracking with Structural Invariance Constraint. In BMVC, 2018.
NOMT
50. using public detections
26.7
33.7
±16.2
44.612.2% 44.0% 7,76232,547442 (9.4)823 (17.5)11.5Public
W. Choi. Near-Online Multi-target Tracking with Aggregated Local Flow Descriptor. In ICCV, 2015.
TrackerAvg RankMOTAIDF1MT MLFPFNID Sw.FragHzDetector
CNNTCM
51. using public detections
34.0
29.6
±13.9
36.811.2% 44.0% 7,78634,733712 (16.4)943 (21.7)1.7Public
B. Wang, K. L. Chan, L. Wang, B. Shuai, Z. Zuo, T. Liu, G. Wang. Joint Learning of Convolutional Neural Networks and Temporally Constrained Metrics for Tracklet Association. In DeepVision Workshop (CVPR), 2016.
MCF_PHD
52. using public detections
33.0
29.9
±20.0
38.211.9% 44.0% 8,89233,529656 (14.4)989 (21.8)12.2Public
N. Wojke, D. Paulus. Global data association for the Probability Hypothesis Density filter using network flows. In 2016 IEEE International Conference on Robotics and Automation, ICRA, 2016.
HAM_INTP15
53. online method using public detections
30.5
28.6
±13.8
41.410.0% 44.0% 7,48535,910460 (11.1)1,038 (25.0)18.7Public
Y. Yoon, A. Boragule, Y. Song, K. Yoon, M. Jeon. Online Multi-Object Tracking with Historical Appearance Matching and Scene Adaptive Detection Filtering. In IEEE AVSS, 2018.
DAC_min
54. online method using public detections
32.6
28.3
±13.4
38.39.8% 45.5% 8,39635,122543 (12.7)1,162 (27.1)11.6Public
RNN_LSTM
55. online method using public detections
57.3
19.0
±15.2
17.15.5% 45.6% 11,57836,7061,490 (37.0)2,081 (51.7)165.2Public
A. Milan, S. Rezatofighi, A. Dick, I. Reid, K. Schindler. Online Multi-Target Tracking using Recurrent Neural Networks. In AAAI, 2017.
TBX
56. using public detections
49.6
27.5
±13.3
33.810.4% 45.8% 7,96835,810759 (18.2)1,528 (36.6)0.1Public
R. Henschel, L. Leal-Taixé, B. Rosenhahn, K. Schindler. Tracking with multi-level features. In arXiv:1607.07304, 2016.
CEM
57. using public detections
48.3
19.3
±17.5
0.08.5% 46.5% 14,18034,591813 (18.6)1,023 (23.4)1.1Public
A. Milan, S. Roth, K. Schindler. Continuous Energy Minimization for Multitarget Tracking. In IEEE TPAMI, 2014.
OMT_DFH
58. online method using public detections
42.3
21.2
±17.2
37.37.1% 46.5% 13,21834,657563 (12.9)1,255 (28.8)28.6Public
J. Ju, D. Kim, B. Ku, D. Han, H. Ko. Online multi-object tracking with efficient track drift and fragmentation handling. In J. Opt. Soc. Am. A, 2017.
TENSOR
59. using public detections
51.6
24.3
±13.2
24.15.5% 46.6% 6,64438,5821,271 (34.2)1,304 (35.1)24.0Public
X. Shi, H. Ling, Y. Pang, W. Hu, P. Chu, J. Xing. Rank-1 Tensor Approximation for High-Order Association in Multi-target Tracking. In IJCV, 2019.
SCEA
60. online method using public detections
38.8
29.1
±12.2
37.28.9% 47.3% 6,06036,912604 (15.1)1,182 (29.6)6.8Public
J. Yoon, C. Lee, M. Yang, K. Yoon. Online Multi-object Tracking via Structural Constraint Event Aggregation. In IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), 2016.
TrackerAvg RankMOTAIDF1MT MLFPFNID Sw.FragHzDetector
TBD
61. using public detections
65.7
15.9
±17.6
0.06.4% 47.9% 14,94334,7771,939 (44.7)1,963 (45.2)0.7Public
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.
SiameseCNN
62. using public detections
38.3
29.0
±15.1
34.38.5% 48.4% 5,16037,798639 (16.6)1,316 (34.2)52.8Public
Laura Leal-Taixé, Cristian Canton-Ferrer, Konrad Schindler. Learning by Tracking: Siamese CNN for Robust Target Association. DeepVision Workshop (CVPR), Las Vegas (Nevada, USA), June 2016.
oICF
63. online method using public detections
44.1
27.1
±14.9
40.56.4% 48.7% 7,59436,757454 (11.3)1,660 (41.3)1.4Public
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.
BiGRU1
64. using public detections
44.3
26.1
±16.5
32.26.5% 48.8% 5,76138,948719 (19.6)2,046 (55.9)4.0Public
Anonymous submission
CppSORT
65. online method using public detections
50.5
21.7
±11.8
26.83.7% 49.1% 8,42238,4541,231 (32.9)2,005 (53.6)1,112.1Public
S. Murray. Real-Time Multiple Object Tracking - A Study on the Importance of Speed. In arXiv preprint arXiv:1709.03572, 2017.
KCF_Simple
66. online method using public detections
59.1
18.3
±11.1
25.12.6% 49.8% 8,97639,8051,436 (40.8)2,634 (74.8)35.6Public
Anonymous submission
UN_DAM
67. online method using public detections
33.3
29.7
±12.3
41.49.2% 49.9% 7,61035,269318 (7.5)674 (15.8)20.7Public
Multi Object Tracking using Deep Structural Cost Minimization in Data Association
RKCF
68. online method using public detections
59.7
16.8
±13.5
29.05.5% 50.1% 10,33639,805980 (27.8)1,750 (49.7)6.2Public
Anonymous submission
DSA_MOT
69. online method using public detections
29.5
29.4
±12.9
41.29.2% 50.2% 7,70535,364329 (7.8)789 (18.6)9.6Public
Anonymous submission
DPT
70. online method using public detections
61.1
16.1
±12.1
27.55.0% 50.3% 10,33040,1541,076 (31.1)1,794 (51.8)0.4Public
TrackerAvg RankMOTAIDF1MT MLFPFNID Sw.FragHzDetector
PoMOT
71. online method using public detections
63.1
16.7
±13.8
28.85.0% 50.3% 10,18540,025968 (27.8)1,748 (50.2)0.3Public
Anonymous submission
HSJ_Sia
72. online method using public detections
53.8
20.9
±13.0
29.24.0% 51.6% 6,45740,4771,695 (49.7)2,734 (80.1)70.3Public
Anonymous submission
SLTV15
73. online method using public detections
35.8
27.6
±15.1
40.37.2% 51.9% 6,58137,566358 (9.2)884 (22.7)20.9Public
Gwangju Institute of Science and Technology(GIST), Machine Learning and Vision Laboratory
MotiCon
74. using public detections
54.5
23.1
±16.4
29.44.7% 52.0% 10,40435,8441,018 (24.4)1,061 (25.5)1.4Public
L. Leal-Taixé, M. Fenzi, A. Kuznetsova, B. Rosenhahn, S. Savarese. Learning an image-based motion context for multiple people tracking. In CVPR, 2014.
AdTobKF
75. online method using public detections
38.3
24.8
±12.1
34.54.0% 52.0% 6,20139,321666 (18.5)1,300 (36.1)206.5Public
K. Loumponias, A. Dimou, N. Vretos, P. Daras. Adaptive Tobit Kalman-Based Tracking. In 2018 14th International Conference on Signal-Image Technology \& Internet-Based Systems (SITIS), 2018.
ALExTRAC
76. using public detections
60.1
17.0
±12.1
17.33.9% 52.4% 9,23339,9331,859 (53.1)1,872 (53.5)3.7Public
A. Bewley, L. Ott, F. Ramos, B. Upcroft. ALExTRAC: Affinity Learning by Exploring Temporal Reinforcement within Association Chains. In International Conference on Robotics and Automation (ICRA), (to appear) 2016.
EAMTTpub
77. online method using public detections
47.8
22.3
±14.2
32.85.4% 52.7% 7,92438,982833 (22.8)1,485 (40.6)12.2Public
R. Sanchez-Matilla, F. Poiesi, A. Cavallaro "Multi-target tracking with strong and weak detections" in BMTT ECCVw 2016
LP_SSVM
78. using public detections
40.5
25.2
±13.7
34.05.8% 53.0% 8,36936,932646 (16.2)849 (21.3)41.3Public
S. Wang, C. Fowlkes. Learning Optimal Parameters for Multi-target Tracking with Contextual Interactions. In International Journal of Computer Vision, 2016.
RMOT
79. online method using public detections
54.0
18.6
±17.5
32.65.3% 53.3% 12,47336,835684 (17.1)1,282 (32.0)7.9Public
J. Yoon, H. Yang, J. Lim, K. Yoon. Bayesian Multi-Object Tracking Using Motion Context from Multiple Objects. In IEEE Winter Conference on Applications of Computer Vision (WACV), 2015.
SMOT
80. using public detections
67.5
18.2
±10.3
0.02.8% 54.8% 8,78040,3101,148 (33.4)2,132 (62.0)2.7Public
C. Dicle, O. Camps, M. Sznaier. The Way They Move: Tracking Targets with Similar Appearance. In ICCV, 2013.
TrackerAvg RankMOTAIDF1MT MLFPFNID Sw.FragHzDetector
DCO_X
81. using public detections
49.7
19.6
±14.1
31.55.1% 54.9% 10,65238,232521 (13.8)819 (21.7)0.3Public
A. Milan, K. Schindler, S. Roth. Multi-Target Tracking by Discrete-Continuous Energy Minimization. In IEEE PAMI, 2016.
GMPHD
82. online method using public detections
48.3
18.5
±12.7
28.43.9% 55.3% 7,86441,766459 (14.3)1,266 (39.5)19.8Public
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.
TC_ODAL
83. online method using public detections
65.4
15.1
±15.0
0.03.2% 55.8% 12,97038,538637 (17.1)1,716 (46.0)1.7Public
S. Bae, K. Yoon. Robust Online Multi-Object Tracking based on Tracklet Confidence and Online Discriminative Appearance Learning. In CVPR, 2014.
FP_H
84. online method using public detections new
45.5
23.4
±12.8
33.73.7% 55.9% 5,78240,719538 (16.0)1,875 (55.6)33.4Public
Anonymous submission
TO
85. using public detections
44.9
25.7
±13.5
32.74.3% 57.4% 4,77940,511383 (11.2)600 (17.6)5.0Public
S. Manen, R. Timofte, D. Dai, L. Gool. Leveraging single for multi-target tracking using a novel trajectory overlap affinity measure. In 2016 IEEE Winter Conference on Applications of Computer Vision (WACV), 2016.
DCOR
86. online method using public detections
47.1
22.4
±12.1
24.73.3% 57.4% 5,60341,410634 (19.4)1,686 (51.7)37.6Public
Anonymous submission
JPDA_m
87. using public detections
39.4
23.8
±15.1
33.85.0% 58.1% 6,37340,084365 (10.5)869 (25.0)32.6Public
H. Rezatofighi, A. Milan, Z. Zhang, Q. Shi, A. Dick, I. Reid. Joint Probabilistic Data Association Revisited. In ICCV, 2015.
HAM_SADF
88. online method using public detections
36.1
25.2
±13.9
37.85.7% 58.3% 7,33038,275357 (9.5)745 (19.8)18.7Public
Y. Yoon, A. Boragule, Y. Song, K. Yoon, M. Jeon. Online Multi-Object Tracking with Historical Appearance Matching and Scene Adaptive Detection Filtering. In IEEE AVSS, 2018.
GSCR
89. online method using public detections
48.7
15.8
±10.5
27.91.8% 61.0% 7,59743,633514 (17.7)1,010 (34.8)28.1Public
L. Fagot-Bouquet, R. Audigier, Y. Dhome, F. Lerasle. Online multi-person tracking based on global sparse collaborative representations. In ICIP, 2015.
SAS_MOT15
90. using public detections
52.8
22.2
±13.8
27.23.1% 61.6% 5,59141,531700 (21.6)1,240 (38.3)8.9Public
A. Maksai, P. Fua. Eliminating Exposure Bias and Metric Mismatch in Multiple Object Tracking. In CVPR, 2019.
TrackerAvg RankMOTAIDF1MT MLFPFNID Sw.FragHzDetector
TFMOT
91. online method using public detections
45.7
23.8
±11.9
32.34.9% 62.0% 4,53341,873404 (12.7)792 (24.9)11.3Public
M. Abhijeet Boragule. Joint Cost Minimization for Multi-Object Tracking. In 2017 IEEE International Conference on Advanced Vide and Signale Based Surveillance, 2017.
DEEPDA_MOT
92. online method using public detections
48.5
22.5
±17.7
25.96.4% 62.0% 7,34639,0921,159 (31.9)1,538 (42.3)172.8Public
K. Yoon, D. Kim, Y. Yoon, M. Jeon. Data Association for Multi-Object Tracking via Deep Neural Networks. In Sensors, 2019.
SegTrack
93. using public detections
53.1
22.5
±15.2
31.55.8% 63.9% 7,89039,020697 (19.1)737 (20.2)0.2Public
A. Milan, L. Leal-Taixé, K. Schindler, I. Reid. Joint Tracking and Segmentation of Multiple Targets. In CVPR, 2015.
LINF1
94. using public detections
39.8
24.5
±15.4
34.85.5% 64.6% 5,86440,207298 (8.6)744 (21.5)7.5Public
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.
Goturn15
95. online method using public detections
52.8
23.9
±14.6
22.33.6% 66.4% 7,02138,750965 (26.1)1,237 (33.5)3.9Public
Anonymous submission
TC_SIAMESE
96. online method using public detections
48.1
20.2
±13.9
32.62.6% 67.5% 6,12742,596294 (9.6)825 (26.9)13.0Public
Y. Yoon, Y. Song, K. Yoon, M. Jeon. Online Multiple-Object Tracking using Selective Deep Appearance Matching. In IEEE/IEIE The International Conference on Consumer Electronics (ICCE) Asia, 2018.
JPDA_OP
97. online method using public detections
44.5
3.6
±11.3
7.50.4% 96.1% 1,02458,18929 (5.5)119 (22.5)77.7Public
Anonymous submission

Benchmark Statistics

SequencesFramesTrajectoriesBoxes
11578372161440

Difficulty Analysis

Sequence difficulty (from easiest to hardest, measured by average MOTA)

TUD-Crossing

TUD-Crossing

(63.9% MOTA)

PETS09-S2L2

PETS09-S2L2

(43.3% MOTA)

ETH-Jelmoli

ETH-Jelmoli

(37.8% MOTA)

...

...

Venice-1

Venice-1

(22.7% MOTA)

ADL-Rundle-1

ADL-Rundle-1

(17.8% 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.