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!


Benchmark Statistics

TrackerMOTAIDF1MTMLFPFNID Sw.FragDetector
UN_DAM
1. online method using public detections
29.7
±12.3
41.49.2% 49.9% 7,61035,269318 (7.5)674 (15.8)Public
Multi Object Tracking using Deep Structural Cost Minimization in Data Association
TSMLCDEnew
2. using public detections
34.3
±13.1
44.114.0% 39.4% 7,86931,908618 (12.9)959 (20.0)Public
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.
TSDA_OAL
3. online method using public detections
18.6
±17.6
36.19.4% 42.3% 16,35032,853806 (17.3)1,544 (33.2)Public
H. Ko. Online multi-person tracking with two-stage data association and online appearance model learning. In IET Computer Vision, 2017.
Tracktor15
4. online method using public detections
44.1
±13.2
46.718.0% 26.2% 6,47726,5771,318 (23.2)1,790 (31.5)Public
P. Bergmann, T. Meinhardt, L. Leal-Taixé. Tracking without bells and whistles. In ICCV, 2019.
TO
5. using public detections
25.7
±13.5
32.74.3% 57.4% 4,77940,511383 (11.2)600 (17.6)Public
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.
TLO
6. using public detections
41.3
±13.7
46.115.7% 34.5% 8,00027,210852 (15.3)1,405 (25.2)Public
Anonymous submission
TLO15
7. online method using public detections
40.0
±14.9
44.317.1% 28.8% 9,34926,3281,207 (21.1)1,624 (28.4)Public
Anonymous submission
TFMOT
8. online method using public detections
23.8
±12.0
32.34.9% 62.0% 4,53341,873404 (12.7)792 (24.9)Public
M. Abhijeet Boragule. Joint Cost Minimization for Multi-Object Tracking. In 2017 IEEE International Conference on Advanced Vide and Signale Based Surveillance, 2017.
TENSOR
9. using public detections
24.3
±13.2
24.15.5% 46.6% 6,64438,5821,271 (34.2)1,304 (35.1)Public
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.
TDAM
10. online method using public detections
33.0
±9.8
46.113.3% 39.1% 10,06430,617464 (9.2)1,506 (30.0)Public
M. Yang, Y. Jia. Temporal dynamic appearance modeling for online multi-person tracking. In Computer Vision and Image Understanding, 2016.
TrackerMOTAIDF1MTMLFPFNID Sw.FragDetector
TC_SIAMESE
11. online method using public detections
20.2
±13.9
32.62.6% 67.5% 6,12742,596294 (9.6)825 (26.9)Public
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.
TC_ODAL
12. online method using public detections
48.3
±12.0
56.532.2% 24.3% 9,64021,629504 (7.8)1,074 (16.6)Public
S. Bae, K. Yoon. Robust Online Multi-Object Tracking based on Tracklet Confidence and Online Discriminative Appearance Learning. In CVPR, 2014.
TBX
13. using public detections
27.5
±13.3
33.810.4% 45.8% 7,96835,810759 (18.2)1,528 (36.6)Public
R. Henschel, L. Leal-Taixé, B. Rosenhahn, K. Schindler. Tracking with multi-level features. In arXiv:1607.07304, 2016.
TBSS15
14. online method using public detections
29.2
±12.5
37.26.8% 43.8% 6,06836,779649 (16.2)1,508 (37.6)Public
X. Zhou, P. Jiang, Z. Wei, H. Dong, F. Wang. Online Multi-Object Tracking with Structural Invariance Constraint. In BMVC, 2018.
TBD
15. using public detections
15.9
±27.4
0.06.4% 47.9% 14,94334,7771,939 (44.7)1,963 (45.2)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.
STRN
16. online method using public detections
38.1
±11.3
46.611.5% 33.4% 5,45131,5711,033 (21.2)2,665 (54.8)Public
J. Xu, Y. Cao, Z. Zhang, H. Hu. Spatial-Temporal Relation Networks for Multi-Object Tracking. In ICCV, 2019.
SRPN
17. online method using public detections
31.0
±13.3
30.712.6% 41.7% 10,24131,0991,062 (21.5)1,370 (27.7)Public
Anonymous submission
SORT_Y
18. online method using public detections
45.9
±22.2
47.220.5% 27.0% 6,24725,6691,346 (23.1)1,436 (24.7)Public
Anonymous submission
SNM
19. online method using public detections
31.3
±16.5
38.212.6% 35.4% 8,90332,393926 (19.6)2,382 (50.4)Public
Anonymous submission
SMOTe
20. online method using public detections
28.0
±16.1
45.415.0% 30.8% 15,88127,372977 (17.6)2,106 (38.0)Public
Anonymous submission
TrackerMOTAIDF1MTMLFPFNID Sw.FragDetector
SMOT
21. using public detections
18.2
±27.4
0.02.8% 54.8% 8,78040,3101,148 (33.4)2,132 (62.0)Public
C. Dicle, O. Camps, M. Sznaier. The Way They Move: Tracking Targets with Similar Appearance. In ICCV, 2013.
SLTV15
22. online method using public detections
27.6
±15.1
40.37.2% 51.9% 6,58137,566358 (9.2)884 (22.7)Public
Gwangju Institute of Science and Technology(GIST), Machine Learning and Vision Laboratory
SiameseCNN
23. using public detections
29.0
±15.1
34.38.5% 48.4% 5,16037,798639 (16.6)1,316 (34.2)Public
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.
siam
24. online method using public detections
33.0
±17.0
36.28.9% 43.3% 5,10135,190853 (20.0)1,078 (25.2)Public
Anonymous submission
SegTrack
25. using public detections
22.5
±15.2
31.55.8% 63.9% 7,89039,020697 (19.1)737 (20.2)Public
A. Milan, L. Leal-Taixé, K. Schindler, I. Reid. Joint Tracking and Segmentation of Multiple Targets. In CVPR, 2015.
SCEA
26. online method using public detections
29.1
±12.5
37.28.9% 47.3% 6,06036,912604 (15.1)1,182 (29.6)Public
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.
SAS_MOT15
27. using public detections
22.2
±13.8
27.23.1% 61.6% 5,59141,531700 (21.6)1,240 (38.3)Public
A. Maksai, P. Fua. Eliminating Exposure Bias and Metric Mismatch in Multiple Object Tracking. In CVPR, 2019.
RSCNN
28. using public detections
29.5
±23.9
37.012.9% 36.3% 11,86630,474976 (19.4)1,176 (23.3)Public
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.
RNN_LSTM
29. online method using public detections
19.0
±20.3
17.15.5% 45.6% 11,57836,7061,490 (37.0)2,081 (51.7)Public
A. Milan, S. Rezatofighi, A. Dick, I. Reid, K. Schindler. Online Multi-Target Tracking using Recurrent Neural Networks. In AAAI, 2017.
RMOT
30. online method using public detections
18.6
±17.5
32.65.3% 53.3% 12,47336,835684 (17.1)1,282 (32.0)Public
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.
TrackerMOTAIDF1MTMLFPFNID Sw.FragDetector
RKCF
31. online method using public detections
16.8
±13.5
29.05.5% 50.1% 10,33639,805980 (27.8)1,750 (49.7)Public
Anonymous submission
RAR15pub
32. online method using public detections
35.1
±12.5
45.413.0% 42.3% 6,77132,717381 (8.1)1,523 (32.6)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.
QuadMOT
33. using public detections
33.8
±14.8
40.412.9% 36.9% 7,89832,061703 (14.7)1,430 (29.9)Public
J. Son, M. Baek, M. Cho, B. Han. Multi-Object Tracking with Quadruplet Convolutional Neural Networks. In CVPR, 2017.
PoMOT
34. online method using public detections
16.7
±13.8
28.85.0% 50.3% 10,18540,025968 (27.8)1,748 (50.2)Public
Anonymous submission
PHD_GSDL
35. online method using public detections
30.5
±14.9
38.87.6% 41.2% 6,53435,284879 (20.6)2,208 (51.9)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.
OMT_DFH
36. online method using public detections
21.2
±17.2
37.37.1% 46.5% 13,21834,657563 (12.9)1,255 (28.8)Public
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.
oICF
37. online method using public detections
27.1
±14.9
40.56.4% 48.7% 7,59436,757454 (11.3)1,660 (41.3)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.
NOMT
38. using public detections
33.7
±16.2
44.612.2% 44.0% 7,76232,547442 (9.4)823 (17.5)Public
W. Choi. Near-Online Multi-target Tracking with Aggregated Local Flow Descriptor. In ICCV, 2015.
MTSTracker
39. online method using public detections
20.6
±18.2
31.99.0% 36.9% 15,16132,2121,387 (29.2)2,357 (49.5)Public
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.
MR
40. using public detections
36.6
±16.6
47.233.1% 21.5% 16,69621,428850 (13.1)1,156 (17.8)Public
Anonymous submission
TrackerMOTAIDF1MTMLFPFNID Sw.FragDetector
MPNTrack15
41. using public detections
48.3
±12.0
56.532.2% 24.3% 9,64021,629504 (7.8)1,074 (16.6)Public
Anonymous submission
MotiCon
42. using public detections
23.1
±16.4
29.44.7% 52.0% 10,40435,8441,018 (24.4)1,061 (25.5)Public
L. Leal-Taixé, M. Fenzi, A. Kuznetsova, B. Rosenhahn, S. Savarese. Learning an image-based motion context for multiple people tracking. In CVPR, 2014.
MMHT15
43. online method using public detections
29.8
±17.0
38.012.1% 38.0% 10,54831,3901,189 (24.3)1,612 (33.0)Public
Anonymous submission
mLK
44. online method using public detections
35.1
±12.9
47.512.3% 38.3% 5,67833,815383 (8.5)1,175 (26.1)Public
Yuan Zhang, Di Xie and Shiliang Pu (Hikvision Research Institute)
MHT__ReID
45. using public detections
33.0
±15.1
46.417.6% 42.6% 8,72532,046421 (8.8)851 (17.8)Public
Anonymous submission
MHT_DAM
46. using public detections
32.4
±15.6
45.316.0% 43.8% 9,06432,060435 (9.1)826 (17.3)Public
C. Kim, F. Li, A. Ciptadi, J. Rehg. Multiple Hypothesis Tracking Revisited. In ICCV, 2015.
MHTREID15
47. using public detections
40.0
±16.2
49.429.7% 24.4% 12,78023,378684 (11.0)1,112 (17.9)Public
Anonymous submission
MDP
48. online method using public detections
30.3
±14.6
44.713.0% 38.4% 9,71732,422680 (14.4)1,500 (31.8)Public
Y. Xiang, A. Alahi, S. Savarese. Learning to Track: Online Multi-Object Tracking by Decision Making. In International Conference on Computer Vision (ICCV), 2015.
MCF_PHD
49. using public detections
29.9
±20.0
38.211.9% 44.0% 8,89233,529656 (14.4)989 (21.8)Public
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.
LP_SSVM
50. using public detections
25.2
±13.7
34.05.8% 53.0% 8,36936,932646 (16.2)849 (21.3)Public
S. Wang, C. Fowlkes. Learning Optimal Parameters for Multi-target Tracking with Contextual Interactions. In International Journal of Computer Vision, 2016.
TrackerMOTAIDF1MTMLFPFNID Sw.FragDetector
LP2D
51. using public detections
19.8
±27.4
0.06.7% 41.2% 11,58036,0451,649 (39.9)1,712 (41.4)Public
MOT baseline: Linear programming on 2D image coordinates.
LINF1
52. using public detections
24.5
±15.4
34.85.5% 64.6% 5,86440,207298 (8.6)744 (21.5)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.
Lif_T
53. using public detections
52.5
±13.4
60.033.8% 25.8% 6,83721,610730 (11.3)1,047 (16.2)Public
Anonymous submission
LFNF
54. using public detections
31.6
±12.3
33.19.6% 41.7% 5,94335,095961 (22.4)1,106 (25.8)Public
Sheng H, Hao L, Chen J, et al. Robust Local Effective Matching Model for Multi-Target Tracking. In PCM, 2017
LDCT
55. online method using public detections
4.7
±41.3
16.811.4% 32.5% 14,06632,15612,348 (259.1)2,918 (61.2)Public
F. Solera, S. Calderara, R. Cucchiara. Learning to Divide and Conquer for Online Multi-Target Tracking. In ICCV, 2015
KCF_Simple
56. online method using public detections
18.3
±11.1
25.12.6% 49.8% 8,97639,8051,436 (40.8)2,634 (74.8)Public
Anonymous submission
KCF
57. online method using public detections
38.9
±14.5
44.516.6% 31.5% 7,32129,501720 (13.9)1,440 (27.7)Public
P. Chu, H. Fan, C. Tan, H. Ling. Online Multi-Object Tracking with Instance-Aware Tracker and Dynamic Model Refreshment. In WACV, 2019.
JPDA_OP
58. online method using public detections
3.6
±11.3
7.50.4% 96.1% 1,02458,18929 (5.5)119 (22.5)Public
Anonymous submission
JPDA_m
59. using public detections
23.8
±15.1
33.85.0% 58.1% 6,37340,084365 (10.5)869 (25.0)Public
H. Rezatofighi, A. Milan, Z. Zhang, Q. Shi, A. Dick, I. Reid. Joint Probabilistic Data Association Revisited. In ICCV, 2015.
JointMC
60. using public detections
35.6
±18.9
45.123.2% 39.3% 10,58028,508457 (8.5)969 (18.1)Public
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.
TrackerMOTAIDF1MTMLFPFNID Sw.FragDetector
ISE_MOT15R
61. online method using public detections
46.7
±16.1
51.629.4% 25.7% 11,00320,839878 (13.3)1,265 (19.1)Public
MIFT
INARLA
62. online method using public detections
34.7
±13.2
42.112.5% 30.0% 9,85529,1581,112 (21.2)2,848 (54.2)Public
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.
HybridDAT
63. online method using public detections
35.0
±15.0
47.711.4% 42.2% 8,45531,140358 (7.3)1,267 (25.7)Public
M. Yang, Y. Jia. A Hybrid Data Association Framework for Robust Online Multi-Object Tracking. In IEEE Transactions on Image Processing, 2016.
HSJ_Sia
64. online method using public detections
20.9
±13.0
29.24.0% 51.6% 6,45740,4771,695 (49.7)2,734 (80.1)Public
Anonymous submission
HOHOTRACK
65. online method using public detections
26.8
±21.7
32.928.6% 16.9% 18,99424,5491,411 (23.5)3,417 (56.9)Public
Anonymous submission
HAM_SADF
66. online method using public detections
25.2
±13.9
37.85.7% 58.3% 7,33038,275357 (9.5)745 (19.8)Public
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.
HAM_INTP15
67. online method using public detections
28.6
±13.8
41.410.0% 44.0% 7,48535,910460 (11.1)1,038 (25.0)Public
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
68. online method using public detections
15.8
±10.5
27.91.8% 61.0% 7,59743,633514 (17.7)1,010 (34.8)Public
L. Fagot-Bouquet, R. Audigier, Y. Dhome, F. Lerasle. Online multi-person tracking based on global sparse collaborative representations. In ICIP, 2015.
Goturn15
69. online method using public detections
23.9
±14.6
22.33.6% 66.4% 7,02138,750965 (26.1)1,237 (33.5)Public
Anonymous submission
GMPHD_OGM
70. online method using public detections
30.7
±12.6
38.811.5% 38.1% 6,50235,0301,034 (24.1)1,351 (31.4)Public
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.
TrackerMOTAIDF1MTMLFPFNID Sw.FragDetector
GMPHD
71. online method using public detections
18.5
±12.7
28.43.9% 55.3% 7,86441,766459 (14.3)1,266 (39.5)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.
GMMA_intp
72. online method using public detections
27.3
±12.0
36.66.5% 43.1% 7,84835,817987 (23.7)1,848 (44.3)Public
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.
FP_H
73. online method using public detections
23.4
±12.8
33.73.7% 55.9% 5,78240,719538 (16.0)1,875 (55.6)Public
Anonymous submission
FFT15
74. online method using public detections
46.3
±14.4
48.829.1% 23.2% 9,87021,9131,232 (19.1)1,638 (25.5)Public
Anonymous submission
ELP
75. using public detections
25.0
±10.8
26.27.5% 43.8% 7,34537,3441,396 (35.6)1,804 (46.0)Public
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.
EDA_GNN
76. online method using public detections
21.8
±13.8
27.89.0% 40.2% 11,97034,5871,488 (34.0)1,851 (42.4)Public
Paper ID 2713
EAMTTpub
77. online method using public detections
22.3
±14.2
32.85.4% 52.7% 7,92438,982833 (22.8)1,485 (40.6)Public
R. Sanchez-Matilla, F. Poiesi, A. Cavallaro "Multi-target tracking with strong and weak detections" in BMTT ECCVw 2016
dSRPN15
78. online method using public detections
33.3
±15.0
32.79.3% 43.7% 7,82532,211919 (19.3)1,276 (26.8)Public
Anonymous submission
DSA_MOT
79. online method using public detections
29.4
±12.9
41.29.2% 50.2% 7,70535,364329 (7.8)789 (18.6)Public
Anonymous submission
DP_NMS
80. using public detections
14.5
±16.5
19.76.0% 40.8% 13,17134,8144,537 (104.7)3,090 (71.3)Public
H. Pirsiavash, D. Ramanan, C. Fowlkes. Globally-Optimal Greedy Algorithms for Tracking a Variable Number of Objects. In CVPR, 2011.
TrackerMOTAIDF1MTMLFPFNID Sw.FragDetector
DPT
81. online method using public detections
16.1
±12.1
27.55.0% 50.3% 10,33040,1541,076 (31.1)1,794 (51.8)Public
DeepMP
82. using public detections
40.5
±12.8
28.816.8% 35.2% 6,27929,654599 (11.6)1,034 (20.0)Public
Anonymous submission
DEEPDA_MOT
83. online method using public detections
22.5
±17.7
25.96.4% 62.0% 7,34639,0921,159 (31.9)1,538 (42.3)Public
K. Yoon, D. Kim, Y. Yoon, M. Jeon. Data Association for Multi-Object Tracking via Deep Neural Networks. In Sensors, 2019.
DCO_X
84. using public detections
19.6
±14.1
31.55.1% 54.9% 10,65238,232521 (13.8)819 (21.7)Public
A. Milan, K. Schindler, S. Roth. Multi-Target Tracking by Discrete-Continuous Energy Minimization. In IEEE PAMI, 2016.
DCOR
85. online method using public detections
22.4
±12.1
24.73.3% 57.4% 5,60341,410634 (19.4)1,686 (51.7)Public
Anonymous submission
DCCRF
86. online method using public detections
33.6
±11.0
39.110.4% 37.6% 5,91734,002866 (19.4)1,566 (35.1)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.
DAC_min
87. online method using public detections
28.3
±13.4
38.39.8% 45.5% 8,39635,122543 (12.7)1,162 (27.1)Public
CRF_RNN15
88. using public detections
38.9
±15.1
49.320.9% 29.4% 10,66926,291591 (10.3)1,270 (22.2)Public
Anonymous submission
CRFTrack_
89. using public detections
40.0
±14.5
49.623.0% 28.6% 10,29525,917658 (11.4)1,508 (26.1)Public
Anonymous submission
CppSORT
90. online method using public detections
21.7
±11.8
26.83.7% 49.1% 8,42238,4541,231 (32.9)2,005 (53.6)Public
S. Murray. Real-Time Multiple Object Tracking - A Study on the Importance of Speed. In arXiv preprint arXiv:1709.03572, 2017.
TrackerMOTAIDF1MTMLFPFNID Sw.FragDetector
CNNTCM
91. using public detections
29.6
±13.9
36.811.2% 44.0% 7,78634,733712 (16.4)943 (21.7)Public
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.
CF_MCMC
92. using public detections
31.4
±11.3
36.410.3% 40.9% 8,79832,541814 (17.3)1,711 (36.4)Public
Anonymous submission
CEM
93. using public detections
48.3
±12.0
56.532.2% 24.3% 9,64021,629504 (7.8)1,074 (16.6)Public
A. Milan, S. Roth, K. Schindler. Continuous Energy Minimization for Multitarget Tracking. In IEEE TPAMI, 2014.
CDA_DDALpb
94. online method using public detections
32.8
±10.6
38.89.7% 42.2% 4,98335,690614 (14.7)1,583 (37.8)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.
BiGRU1
95. using public detections
26.1
±16.5
32.26.5% 48.8% 5,76138,948719 (19.6)2,046 (55.9)Public
Anonymous submission
AP_HWDPL_p
96. online method using public detections
38.5
±9.9
47.18.7% 37.4% 4,00533,203586 (12.8)1,263 (27.5)Public
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.
AMIR15
97. online method using public detections
37.6
±12.5
46.015.8% 26.8% 7,93329,3971,026 (19.7)2,024 (38.8)Public
A. Sadeghian, A. Alahi, S. Savarese. Tracking The Untrackable: Learning To Track Multiple Cues with Long-Term Dependencies. In ICCV, 2017.
AM
98. online method using public detections
34.3
±13.7
48.311.4% 43.4% 5,15434,848348 (8.0)1,463 (33.8)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.
ALExTRAC
99. using public detections
17.0
±12.1
17.33.9% 52.4% 9,23339,9331,859 (53.1)1,872 (53.5)Public
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.
AdTobKF
100. online method using public detections
24.8
±12.1
34.54.0% 52.0% 6,20139,321666 (18.5)1,300 (36.1)Public
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.
SequencesFramesTrajectoriesBoxes
11578372161440

Difficulty Analysis

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

TUD-Crossing

TUD-Crossing

(0.0% MOTA)

PETS09-S2L2

PETS09-S2L2

(0.0% MOTA)

ETH-Jelmoli

ETH-Jelmoli

(0.0% MOTA)

...

...

KITTI-19

KITTI-19

(0.0% MOTA)

Venice-1

Venice-1

(0.0% MOTA)


Evaluation Measures

Lower is better. Higher is better.
Measure Better Perfect Description
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.