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 RankMOTAIDF1MTMLFPFNID Sw.FragHzDetector
JPDA_OP
1. online method using public detections
41.8
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
45.8
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.
TC_SIAMESE
3. online method using public detections
45.2
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.
TFMOT
4. online method using public detections
43.2
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.
GMPHD
5. online method using public detections
45.5
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.
SAS_MOT15
6. using public detections
49.6
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.
DCOR
7. online method using public detections
44.2
22.4
±12.1
24.73.3% 57.4% 5,60341,410634 (19.4)1,686 (51.7)37.6Public
Anonymous submission
TO
8. using public detections
42.4
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.
HSJ_Sia
9. online method using public detections
50.4
20.9
±13.0
29.24.0% 51.6% 6,45740,4771,695 (49.7)2,734 (80.1)70.3Public
Anonymous submission
SMOT
10. using public detections
63.1
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 RankMOTAIDF1MTMLFPFNID Sw.FragHzDetector
LINF1
11. using public detections
37.6
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.
DPT
12. online method using public detections
57.2
16.1
±12.1
27.55.0% 50.3% 10,33040,1541,076 (31.1)1,794 (51.8)0.4Public
JPDA_m
13. using public detections
37.3
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.
PoMOT
14. online method using public detections
59.0
16.7
±13.8
28.85.0% 50.3% 10,18540,025968 (27.8)1,748 (50.2)0.3Public
Anonymous submission
ALExTRAC
15. using public detections
56.3
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.
KCF_Simple
16. online method using public detections
55.3
18.3
±11.1
25.12.6% 49.8% 8,97639,8051,436 (40.8)2,634 (74.8)35.6Public
Anonymous submission
RKCF
17. online method using public detections
55.8
16.8
±13.5
29.05.5% 50.1% 10,33639,805980 (27.8)1,750 (49.7)6.2Public
Anonymous submission
AdTobKF
18. online method using public detections
36.2
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.
DEEPDA_MOT
19. online method using public detections
45.3
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
20. using public detections
49.8
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.
TrackerAvg RankMOTAIDF1MTMLFPFNID Sw.FragHzDetector
EAMTTpub
21. online method using public detections
44.9
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
BiGRU1
22. using public detections new
41.6
26.1
±16.5
32.26.5% 48.8% 5,76138,948719 (19.6)2,046 (55.9)4.0Public
Anonymous submission
TENSOR
23. using public detections
48.5
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.
TC_ODAL
24. online method using public detections
61.3
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.
CppSORT
25. online method using public detections
47.3
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.
HAM_SADF
26. online method using public detections
34.0
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.
DCO_X
27. using public detections
46.8
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.
SiameseCNN
28. using public detections
36.1
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.
SLTV15
29. online method using public detections
33.8
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
ELP
30. using public detections
45.5
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.
TrackerAvg RankMOTAIDF1MTMLFPFNID Sw.FragHzDetector
LP_SSVM
31. using public detections
38.3
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.
SCEA
32. online method using public detections
36.5
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.
RMOT
33. online method using public detections
50.8
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.
TBSS15
34. online method using public detections
38.4
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.
oICF
35. online method using public detections
41.2
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.
RNN_LSTM
36. online method using public detections
53.4
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.
LP2D
37. using public detections
48.6
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.
HAM_INTP15
38. online method using public detections
28.6
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.
MotiCon
39. using public detections
50.9
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.
GMMA_intp
40. online method using public detections
37.6
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.
TrackerAvg RankMOTAIDF1MTMLFPFNID Sw.FragHzDetector
TBX
41. using public detections
46.4
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.
CDA_DDALpb
42. online method using public detections
29.2
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.
DSA_MOT
43. online method using public detections
27.8
29.4
±12.9
41.29.2% 50.2% 7,70535,364329 (7.8)789 (18.6)9.6Public
Anonymous submission
PHD_GSDL
44. online method using public detections
38.5
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.
UN_DAM
45. online method using public detections
31.0
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
siam
46. online method using public detections
35.7
33.0
±17.0
36.28.9% 43.3% 5,10135,190853 (20.0)1,078 (25.2)1.9Public
Anonymous submission
DAC_min
47. online method using public detections
30.5
28.3
±13.4
38.39.8% 45.5% 8,39635,122543 (12.7)1,162 (27.1)11.6Public
LFNF
48. using public detections
33.5
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
GMPHD_OGM
49. online method using public detections
27.3
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.
AM
50. online method using public detections
23.4
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.
TrackerAvg RankMOTAIDF1MTMLFPFNID Sw.FragHzDetector
DP_NMS
51. using public detections
48.8
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.
TBD
52. using public detections
61.3
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.
CNNTCM
53. using public detections
31.8
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.
OMT_DFH
54. online method using public detections
39.3
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.
CEM
55. using public detections
45.1
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.
EDA_GNN
56. online method using public detections
43.0
21.8
±13.8
27.89.0% 40.2% 11,97034,5871,488 (34.0)1,851 (42.4)56.4Public
Paper ID 2713
DCCRF
57. online method using public detections
31.3
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.
mLK
58. online method using public detections
23.7
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)
MCF_PHD
59. using public detections
30.8
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.
AP_HWDPL_p
60. online method using public detections
19.5
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.
TrackerAvg RankMOTAIDF1MTMLFPFNID Sw.FragHzDetector
TSDA_OAL
61. online method using public detections
45.3
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.
RAR15pub
62. online method using public detections
26.8
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.
NOMT
63. using public detections
24.8
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.
CF_MCMC
64. using public detections
34.0
31.4
±11.3
36.410.3% 40.9% 8,79832,541814 (17.3)1,711 (36.4)3.2Public
Anonymous submission
MDP
65. online method using public detections
32.8
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.
SNM
66. online method using public detections
35.9
31.3
±16.5
38.212.6% 35.4% 8,90332,393926 (19.6)2,382 (50.4)14.8Public
Anonymous submission
MTSTracker
67. online method using public detections
45.8
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.
LDCT
68. online method using public detections
47.2
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
QuadMOT
69. using public detections
30.3
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.
MHT_DAM
70. using public detections
27.8
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.
TrackerAvg RankMOTAIDF1MTMLFPFNID Sw.FragHzDetector
MHT__ReID
71. using public detections
26.8
33.0
±15.1
46.417.6% 42.6% 8,72532,046421 (8.8)851 (17.8)0.3Public
Anonymous submission
TSMLCDEnew
72. using public detections
27.9
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.
STRN
73. online method using public detections
29.1
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
74. online method using public detections
48.8
11.8
±18.5
26.110.0% 33.4% 19,80331,4762,893 (59.3)3,801 (77.9)334.8Public
Anonymous submission
MMHT15
75. online method using public detections
35.4
29.8
±17.0
38.012.1% 38.0% 10,54831,3901,189 (24.3)1,612 (33.0)12.1Public
Anonymous submission
HybridDAT
76. online method using public detections
24.0
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.
TDAM
77. online method using public detections
30.2
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.
RSCNN
78. using public detections
35.3
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.
DeepMP
79. using public detections
20.2
40.5
±12.8
28.816.8% 35.2% 6,27929,654599 (11.6)1,034 (20.0)9.6Public
Anonymous submission
KCF
80. online method using public detections
25.8
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.
TrackerAvg RankMOTAIDF1MTMLFPFNID Sw.FragHzDetector
AMIR15
81. online method using public detections
26.1
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.
INARLA
82. online method using public detections
33.4
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.
JointMC
83. using public detections
24.5
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.
SMOTe
84. online method using public detections
38.7
28.0
±16.1
45.415.0% 30.8% 15,88127,372977 (17.6)2,106 (38.0)1.6Public
Anonymous submission
Tracktor15
85. online method using public detections
30.3
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.
TLO15
86. online method using public detections new
30.5
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
87. using public detections
20.3
38.9
±15.1
49.320.9% 29.4% 10,66926,291591 (10.3)1,270 (22.2)3.2Public
Anonymous submission
CRFTrack_
88. using public detections
21.0
40.0
±14.5
49.623.0% 28.6% 10,29525,917658 (11.4)1,508 (26.1)3.2Public
Anonymous submission
HOHOTRACK
89. online method using public detections
35.3
26.8
±21.7
32.928.6% 16.9% 18,99424,5491,411 (23.5)3,417 (56.9)26.7Public
Anonymous submission
MHTREID15
90. using public detections
24.3
40.0
±16.2
49.429.7% 24.4% 12,78023,378684 (11.0)1,112 (17.9)0.5Public
Anonymous submission
TrackerAvg RankMOTAIDF1MTMLFPFNID Sw.FragHzDetector
MR
91. using public detections
28.0
36.6
±16.6
47.233.1% 21.5% 16,69621,428850 (13.1)1,156 (17.8)0.3Public
Anonymous submission

Benchmark Statistics

SequencesFramesTrajectoriesBoxes
11578372161440

Difficulty Analysis

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

TUD-Crossing

TUD-Crossing

(63.4% MOTA)

PETS09-S2L2

PETS09-S2L2

(42.9% MOTA)

ETH-Jelmoli

ETH-Jelmoli

(37.4% MOTA)

...

...

Venice-1

Venice-1

(21.5% MOTA)

ADL-Rundle-1

ADL-Rundle-1

(16.8% 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.