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

TrackerMOTAIDF1 MTMLFPFNID Sw.FragHzDetector
JPDA_OP
1. online method using public detections
3.6
±11.3
7.50.4% 96.1% 1,02458,18929 (5.5)119 (22.5)77.7Public
Anonymous submission
GSCR
2. online method using public detections
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.
KCF_Simple
3. online method using public detections
18.3
±11.1
25.12.6% 49.8% 8,97639,8051,436 (40.8)2,634 (74.8)35.6Public
Anonymous submission
TC_SIAMESE
4. online method using public detections
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.
SMOT
5. using public detections
18.2
±27.4
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.
SAS_MOT15
6. using public detections
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.
TC_ODAL
7. online method using public detections
15.1
±27.4
0.03.2% 55.8% 12,97038,538637 (17.1)1,716 (46.0)1.5Public
S. Bae, K. Yoon. Robust Online Multi-Object Tracking based on Tracklet Confidence and Online Discriminative Appearance Learning. In CVPR, 2014.
DCOR
8. online method using public detections
22.4
±12.1
24.73.3% 57.4% 5,60341,410634 (19.4)1,686 (51.7)37.6Public
Anonymous submission
Goturn15
9. online method using public detections
23.9
±14.6
22.33.6% 66.4% 7,02138,750965 (26.1)1,237 (33.5)3.9Public
Anonymous submission
CppSORT
10. online method using public detections
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.
TrackerMOTAIDF1 MTMLFPFNID Sw.FragHzDetector
FP_H
11. online method using public detections
23.4
±12.8
33.73.7% 55.9% 5,78240,719538 (16.0)1,875 (55.6)33.4Public
Anonymous submission
ALExTRAC
12. using public detections
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.
GMPHD
13. online method using public detections
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.
AdTobKF
14. online method using public detections
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.
HSJ_Sia
15. online method using public detections
20.9
±13.0
29.24.0% 51.6% 6,45740,4771,695 (49.7)2,734 (80.1)70.3Public
Anonymous submission
TO
16. using public detections
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.
MotiCon
17. using public detections
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.
TFMOT
18. online method using public detections
23.8
±12.0
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.
DPT
19. online method using public detections
16.1
±12.1
27.55.0% 50.3% 10,33040,1541,076 (31.1)1,794 (51.8)0.4Public
JPDA_m
20. using public detections
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.
TrackerMOTAIDF1 MTMLFPFNID Sw.FragHzDetector
PoMOT
21. online method using public detections
16.7
±13.8
28.85.0% 50.3% 10,18540,025968 (27.8)1,748 (50.2)0.3Public
Anonymous submission
DCO_X
22. using public detections
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.
RMOT
23. online method using public detections
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.
EAMTTpub
24. online method using public detections
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
LINF1
25. using public detections
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.
RKCF
26. online method using public detections
16.8
±13.5
29.05.5% 50.1% 10,33639,805980 (27.8)1,750 (49.7)6.2Public
Anonymous submission
RNN_LSTM
27. online method using public detections
19.0
±20.3
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.
TENSOR
28. using public detections
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.
HAM_SADF
29. online method using public detections
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.
LP_SSVM
30. using public detections
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.
TrackerMOTAIDF1 MTMLFPFNID Sw.FragHzDetector
SegTrack
31. using public detections
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.
DP_NMS
32. using public detections
14.5
±16.5
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.
DEEPDA_MOT
33. online method using public detections
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.
oICF
34. online method using public detections
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.
TBD
35. using public detections
15.9
±27.4
0.06.4% 47.9% 14,94334,7771,939 (44.7)1,963 (45.2)infPublic
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.
BiGRU1
36. using public detections
26.1
±16.5
32.26.5% 48.8% 5,76138,948719 (19.6)2,046 (55.9)4.0Public
Anonymous submission
GMMA_intp
37. online method using public detections
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.
LP2D
38. using public detections
19.8
±27.4
0.06.7% 41.2% 11,58036,0451,649 (39.9)1,712 (41.4)infPublic
MOT baseline: Linear programming on 2D image coordinates.
TBSS15
39. online method using public detections
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.
OMT_DFH
40. online method using public detections
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.
TrackerMOTAIDF1 MTMLFPFNID Sw.FragHzDetector
SLTV15
41. online method using public detections
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
ELP
42. using public detections
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.
PHD_GSDL
43. online method using public detections
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.
CEM
44. using public detections
19.3
±27.4
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.
SiameseCNN
45. using public detections
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.
AP_HWDPL_p
46. online method using public detections
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.
SCEA
47. online method using public detections
29.1
±12.5
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.
siam
48. online method using public detections
33.0
±17.0
36.28.9% 43.3% 5,10135,190853 (20.0)1,078 (25.2)1.9Public
Anonymous submission
EDA_GNN
49. online method using public detections
21.8
±13.8
27.89.0% 40.2% 11,97034,5871,488 (34.0)1,851 (42.4)56.4Public
Paper ID 2713
MTSTracker
50. online method using public detections
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.
TrackerMOTAIDF1 MTMLFPFNID Sw.FragHzDetector
DSA_MOT
51. online method using public detections
29.4
±12.9
41.29.2% 50.2% 7,70535,364329 (7.8)789 (18.6)9.6Public
Anonymous submission
UN_DAM
52. online method using public detections
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
dSRPN15
53. online method using public detections
33.3
±15.0
32.79.3% 43.7% 7,82532,211919 (19.3)1,276 (26.8)3.9Public
Anonymous submission
TSDA_OAL
54. online method using public detections
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.
LFNF
55. using public detections
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
CDA_DDALpb
56. online method using public detections
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.
DAC_min
57. online method using public detections
28.3
±13.4
38.39.8% 45.5% 8,39635,122543 (12.7)1,162 (27.1)11.6Public
HAM_INTP15
58. online method using public detections
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.
CF_MCMC
59. using public detections
31.4
±11.3
36.410.3% 40.9% 8,79832,541814 (17.3)1,711 (36.4)3.2Public
Anonymous submission
DCCRF
60. online method using public detections
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.
TrackerMOTAIDF1 MTMLFPFNID Sw.FragHzDetector
TBX
61. using public detections
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.
CNNTCM
62. using public detections
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.
AM
63. online method using public detections
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.
HybridDAT
64. online method using public detections
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.
LDCT
65. online method using public detections
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
GMPHD_OGM
66. online method using public detections
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.
STRN
67. online method using public detections
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.
MCF_PHD
68. using public detections
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.
MMHT15
69. online method using public detections
29.8
±17.0
38.012.1% 38.0% 10,54831,3901,189 (24.3)1,612 (33.0)12.1Public
Anonymous submission
NOMT
70. using public detections
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.
TrackerMOTAIDF1 MTMLFPFNID Sw.FragHzDetector
mLK
71. online method using public detections
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)
INARLA
72. online method using public detections
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.
SNM
73. online method using public detections
31.3
±16.5
38.212.6% 35.4% 8,90332,393926 (19.6)2,382 (50.4)14.8Public
Anonymous submission
SRPN
74. online method using public detections
31.0
±13.3
30.712.6% 41.7% 10,24131,0991,062 (21.5)1,370 (27.7)3.9Public
Anonymous submission
QuadMOT
75. using public detections
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.
RSCNN
76. using public detections
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.
MDP
77. online method using public detections
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.
RAR15pub
78. online method using public detections
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.
TDAM
79. online method using public detections
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.
TSMLCDEnew
80. using public detections
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.
TrackerMOTAIDF1 MTMLFPFNID Sw.FragHzDetector
SMOTe
81. online method using public detections
28.0
±16.1
45.415.0% 30.8% 15,88127,372977 (17.6)2,106 (38.0)1.6Public
Anonymous submission
TLO
82. using public detections
41.3
±13.7
46.115.7% 34.5% 8,00027,210852 (15.3)1,405 (25.2)5.0Public
Anonymous submission
AMIR15
83. online method using public detections
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.
MHT_DAM
84. using public detections
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.
KCF
85. online method using public detections
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.
DeepMP
86. using public detections
40.5
±12.8
28.816.8% 35.2% 6,27929,654599 (11.6)1,034 (20.0)9.6Public
Anonymous submission
TLO15
87. online method using public detections
40.0
±14.9
44.317.1% 28.8% 9,34926,3281,207 (21.1)1,624 (28.4)24.6Public
Anonymous submission
MHT__ReID
88. using public detections
33.0
±15.1
46.417.6% 42.6% 8,72532,046421 (8.8)851 (17.8)0.3Public
Anonymous submission
Tracktor++
89. online method using public detections
44.1
±13.2
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.
Tracktor++v2
90. online method using public detections
46.6
±10.0
47.618.2% 27.9% 4,62426,8961,290 (22.9)1,702 (30.3)1.4Public
P. Bergmann, T. Meinhardt, L. Leal-Taixé. Tracking without bells and whistles. In ICCV, 2019.
TrackerMOTAIDF1 MTMLFPFNID Sw.FragHzDetector
SORT_Y
91. online method using public detections
45.9
±22.2
47.220.5% 27.0% 6,24725,6691,346 (23.1)1,436 (24.7)334.8Public
Anonymous submission
CRF_RNN15
92. using public detections
38.9
±15.1
49.320.9% 29.4% 10,66926,291591 (10.3)1,270 (22.2)3.2Public
Anonymous submission
CRFTrack_
93. using public detections
40.0
±14.5
49.623.0% 28.6% 10,29525,917658 (11.4)1,508 (26.1)3.2Public
Anonymous submission
JointMC
94. using public detections
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.
HOHOTRACK
95. online method using public detections
26.8
±21.7
32.928.6% 16.9% 18,99424,5491,411 (23.5)3,417 (56.9)26.7Public
Anonymous submission
FFT15
96. online method using public detections
46.3
±14.4
48.829.1% 23.2% 9,87021,9131,232 (19.1)1,638 (25.5)2.5Public
Anonymous submission
TARCA
97. online method using public detections
48.7
±12.8
58.429.3% 23.4% 8,85522,110567 (8.9)1,147 (17.9)5.9Public
Anonymous submission
ISE_MOT15R
98. online method using public detections
46.7
±16.1
51.629.4% 25.7% 11,00320,839878 (13.3)1,265 (19.1)6.7Public
MIFT
MHTREID15
99. using public detections
40.0
±16.2
49.429.7% 24.4% 12,78023,378684 (11.0)1,112 (17.9)0.5Public
Anonymous submission
MPNTrack15
100. using public detections
48.3
±12.0
56.532.2% 24.3% 9,64021,629504 (7.8)1,074 (16.6)9.3Public
Anonymous submission
TrackerMOTAIDF1 MTMLFPFNID Sw.FragHzDetector
MR
101. using public detections
36.6
±16.6
47.233.1% 21.5% 16,69621,428850 (13.1)1,156 (17.8)0.3Public
Anonymous submission
Lif_T
102. using public detections
52.5
±13.4
60.033.8% 25.8% 6,83721,610730 (11.3)1,047 (16.2)1.5Public
Anonymous submission
SC2D
103. online method using public detections new
50.1
±19.0
55.748.4% 10.3% 15,31214,531797 (10.4)1,816 (23.8)22.6Public
Anonymous submission
SequencesFramesTrajectoriesBoxes
11578372161440

Difficulty Analysis

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

TUD-Crossing

TUD-Crossing

(65.3% MOTA)

PETS09-S2L2

PETS09-S2L2

(44.2% MOTA)

ETH-Jelmoli

ETH-Jelmoli

(39.4% MOTA)

...

...

KITTI-19

KITTI-19

(25.2% MOTA)

ADL-Rundle-1

ADL-Rundle-1

(19.8% 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. The frequency is provided by the authors and not officially evaluated by the MOTChallenge.

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.