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

TrackerMOTAIDF1MOTPMTMLFPFNRecall PrecisionFAFID Sw.FragHz
TSDA_OAL
1. online method using public detections
18.6
±0.0
36.1
±0.0
69.7 68 (9.4)305 (42.3)16,350 32,853 46.5 63.6 2.8 806 (17.3)1,544 (33.2)19.7
H. Ko. Online multi-person tracking with two-stage data association and online appearance model learning. In IET Computer Vision, 2017.
TC_ODAL
2. online method using public detections
15.1
±37.1
0.0
±0.0
70.5 23 (3.2)402 (55.8)12,970 38,538 37.3 63.8 2.2 637 (17.1)1,716 (46.0)1.5
S. Bae, K. Yoon. Robust Online Multi-Object Tracking based on Tracklet Confidence and Online Discriminative Appearance Learning. In CVPR, 2014.
TBD
3. using public detections
15.9
±37.1
0.0
±0.0
70.9 46 (6.4)345 (47.9)14,943 34,777 43.4 64.1 2.6 1,939 (44.7)1,963 (45.2)inf
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.
CEM
4. using public detections
19.3
±37.1
0.0
±0.0
70.7 61 (8.5)335 (46.5)14,180 34,591 43.7 65.4 2.5 813 (18.6)1,023 (23.4)1.1
A. Milan, S. Roth, K. Schindler. Continuous Energy Minimization for Multitarget Tracking. In IEEE TPAMI, 2014.
MTSTracker
5. online method using public detections
20.6
±18.2
31.9
±20.6
70.3 65 (9.0)266 (36.9)15,161 32,212 47.6 65.8 2.6 1,387 (29.2)2,357 (49.5)19.3
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.
RMOT
6. online method using public detections
18.6
±0.0
32.6
±0.0
69.6 38 (5.3)384 (53.3)12,473 36,835 40.0 66.4 2.2 684 (17.1)1,282 (32.0)7.9
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.
DP_NMS
7. using public detections
14.5
±15.2
19.7
±6.5
70.8 43 (6.0)294 (40.8)13,171 34,814 43.3 66.9 2.3 4,537 (104.7)3,090 (71.3)444.8
H. Pirsiavash, D. Ramanan, C. Fowlkes. Globally-Optimal Greedy Algorithms for Tracking a Variable Number of Objects. In CVPR, 2011.
OMT_DFH
8. online method using public detections
21.2
±0.0
37.3
±0.0
69.9 51 (7.1)335 (46.5)13,218 34,657 43.6 67.0 2.3 563 (12.9)1,255 (28.8)28.6
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.
LDCT
9. online method using public detections
4.7
±41.3
16.8
±18.8
71.7 82 (11.4)234 (32.5)14,066 32,156 47.7 67.6 2.4 12,348 (259.1)2,918 (61.2)20.7
F. Solera, S. Calderara, R. Cucchiara. Learning to Divide and Conquer for Online Multi-Target Tracking. In ICCV, 2015
RNN_LSTM
10. online method using public detections
19.0
±18.1
17.1
±8.3
71.0 40 (5.5)329 (45.6)11,578 36,706 40.3 68.1 2.0 1,490 (37.0)2,081 (51.7)165.2
A. Milan, S. Rezatofighi, A. Dick, I. Reid, K. Schindler. Online Multi-Target Tracking using Recurrent Neural Networks. In AAAI, 2017.
TrackerMOTAIDF1MOTPMTMLFPFNRecall PrecisionFAFID Sw.FragHz
DCO_X
11. using public detections
19.6
±0.0
31.5
±0.0
71.4 37 (5.1)396 (54.9)10,652 38,232 37.8 68.5 1.8 521 (13.8)819 (21.7)0.3
A. Milan, K. Schindler, S. Roth. Multi-Target Tracking by Discrete-Continuous Energy Minimization. In IEEE PAMI, 2016.
LP2D
12. using public detections
19.8
±37.1
0.0
±0.0
71.2 48 (6.7)297 (41.2)11,580 36,045 41.3 68.7 2.0 1,649 (39.9)1,712 (41.4)inf
MOT baseline: Linear programming on 2D image coordinates.
EDA_GNN
13. online method using public detections
21.8
±13.8
27.8
±5.8
70.5 65 (9.0)290 (40.2)11,970 34,587 43.7 69.2 2.1 1,488 (34.0)1,851 (42.4)56.4
Paper ID 2713
ALExTRAC
14. using public detections
17.0
±12.1
17.3
±8.8
71.2 28 (3.9)378 (52.4)9,233 39,933 35.0 70.0 1.6 1,859 (53.1)1,872 (53.5)3.7
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.
GSCR
15. online method using public detections
15.8
±10.8
27.9
±10.1
69.4 13 (1.8)440 (61.0)7,597 43,633 29.0 70.1 1.3 514 (17.7)1,010 (34.8)28.1
L. Fagot-Bouquet, R. Audigier, Y. Dhome, F. Lerasle. Online multi-person tracking based on global sparse collaborative representations. In ICIP, 2015.
SMOT
16. using public detections
18.2
±37.1
0.0
±0.0
71.2 20 (2.8)395 (54.8)8,780 40,310 34.4 70.6 1.5 1,148 (33.4)2,132 (62.0)2.7
C. Dicle, O. Camps, M. Sznaier. The Way They Move: Tracking Targets with Similar Appearance. In ICCV, 2013.
Tracker
17. online method using public detections
16.8
±11.3
22.2
±6.1
70.1 15 (2.1)389 (54.0)8,519 40,697 33.8 70.9 1.5 1,917 (56.8)2,416 (71.6)9.5
Anonymous submission
MotiCon
18. using public detections
23.1
±16.4
29.4
±9.8
70.9 34 (4.7)375 (52.0)10,404 35,844 41.7 71.1 1.8 1,018 (24.4)1,061 (25.5)1.4
L. Leal-Taixé, M. Fenzi, A. Kuznetsova, B. Rosenhahn, S. Savarese. Learning an image-based motion context for multiple people tracking. In CVPR, 2014.
GMPHD
19. online method using public detections
18.5
±12.7
28.4
±8.4
70.9 28 (3.9)399 (55.3)7,864 41,766 32.0 71.4 1.4 459 (14.3)1,266 (39.5)19.8
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.
RSCNN
20. using public detections
29.5
±23.4
37.0
±11.4
73.1 93 (12.9)262 (36.3)11,866 30,474 50.4 72.3 2.1 976 (19.4)1,176 (23.3)4.0
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.
TrackerMOTAIDF1MOTPMTMLFPFNRecall PrecisionFAFID Sw.FragHz
CppSORT
21. online method using public detections
21.7
±11.8
26.8
±9.3
71.2 27 (3.7)354 (49.1)8,422 38,454 37.4 73.2 1.5 1,231 (32.9)2,005 (53.6)1,112.1
S. Murray. Real-Time Multiple Object Tracking - A Study on the Importance of Speed. In arXiv preprint arXiv:1709.03572, 2017.
RAT
22. using public detections
19.0
±10.6
26.1
±7.7
70.2 16 (2.2)369 (51.2)7,534 40,858 33.5 73.2 1.3 1,401 (41.8)1,826 (54.5)21.8
Anonymous submission
EAMTTpub
23. online method using public detections
22.3
±14.2
32.8
±13.0
70.8 39 (5.4)380 (52.7)7,924 38,982 36.6 73.9 1.4 833 (22.8)1,485 (40.6)12.2
R. Sanchez-Matilla, F. Poiesi, A. Cavallaro "Multi-target tracking with strong and weak detections" in BMTT ECCVw 2016
SegTrack
24. using public detections
22.5
±0.0
31.5
±0.0
71.7 42 (5.8)461 (63.9)7,890 39,020 36.5 74.0 1.4 697 (19.1)737 (20.2)0.2
A. Milan, L. Leal-Taixé, K. Schindler, I. Reid. Joint Tracking and Segmentation of Multiple Targets. In CVPR, 2015.
LP_SSVM
25. using public detections
25.2
±13.7
34.0
±9.7
71.7 42 (5.8)382 (53.0)8,369 36,932 39.9 74.5 1.4 646 (16.2)849 (21.3)41.3
S. Wang, C. Fowlkes. Learning Optimal Parameters for Multi-target Tracking with Contextual Interactions. In International Journal of Computer Vision, 2016.
MDP
26. online method using public detections
30.3
±0.0
44.7
±0.0
71.3 94 (13.0)277 (38.4)9,717 32,422 47.2 74.9 1.7 680 (14.4)1,500 (31.8)1.1
Y. Xiang, A. Alahi, S. Savarese. Learning to Track: Online Multi-Object Tracking by Decision Making. In International Conference on Computer Vision (ICCV), 2015.
DEEPDA_MOT
27. online method using public detections
22.5
±0.0
25.9
±0.0
70.9 46 (6.4)447 (62.0)7,346 39,092 36.4 75.3 1.3 1,159 (31.9)1,538 (42.3)172.8
K. Yoon, D. Kim, Y. Yoon, M. Jeon. Data Association for Multi-Object Tracking via Deep Neural Networks. In Sensors, 2019.
TDAM
28. online method using public detections
33.0
±0.0
46.1
±0.0
72.8 96 (13.3)282 (39.1)10,064 30,617 50.2 75.4 1.7 464 (9.2)1,506 (30.0)5.9
M. Yang, Y. Jia. Temporal dynamic appearance modeling for online multi-person tracking. In Computer Vision and Image Understanding, 2016.
SC2D
29. online method using public detections
50.1
±19.0
55.7
±7.7
77.2 349 (48.4)74 (10.3)15,312 14,531 76.3 75.4 2.7 797 (10.4)1,816 (23.8)22.6
Anonymous submission
TC_SIAMESE
30. online method using public detections
20.2
±12.6
32.6
±13.4
71.1 19 (2.6)487 (67.5)6,127 42,596 30.7 75.5 1.1 294 (9.6)825 (26.9)13.0
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.
TrackerMOTAIDF1MOTPMTMLFPFNRecall PrecisionFAFID Sw.FragHz
JointMC
31. using public detections
35.6
±17.5
45.1
±9.8
71.9 167 (23.2)283 (39.3)10,580 28,508 53.6 75.7 1.8 457 (8.5)969 (18.1)0.6
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.
MCF_PHD
32. using public detections
29.9
±20.0
38.2
±13.4
71.7 86 (11.9)317 (44.0)8,892 33,529 45.4 75.8 1.5 656 (14.4)989 (21.8)12.2
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_SADF
33. online method using public detections
25.2
±13.4
37.8
±12.2
71.4 41 (5.7)420 (58.3)7,330 38,275 37.7 76.0 1.3 357 (9.5)745 (19.8)18.7
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.
TBX
34. using public detections
27.5
±13.3
33.8
±12.1
70.6 75 (10.4)330 (45.8)7,968 35,810 41.7 76.3 1.4 759 (18.2)1,528 (36.6)0.1
R. Henschel, L. Leal-Taixé, B. Rosenhahn, K. Schindler. Tracking with multi-level features. In arXiv:1607.07304, 2016.
MHT_DAM
35. using public detections
32.4
±0.0
45.3
±0.0
71.8 115 (16.0)316 (43.8)9,064 32,060 47.8 76.4 1.6 435 (9.1)826 (17.3)0.7
C. Kim, F. Li, A. Ciptadi, J. Rehg. Multiple Hypothesis Tracking Revisited. In ICCV, 2015.
oICF
36. online method using public detections
27.1
±14.9
40.5
±12.6
70.0 46 (6.4)351 (48.7)7,594 36,757 40.2 76.5 1.3 454 (11.3)1,660 (41.3)1.4
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.
GMMA_intp
37. online method using public detections
27.3
±12.1
36.6
±8.5
70.9 47 (6.5)311 (43.1)7,848 35,817 41.7 76.6 1.4 987 (23.7)1,848 (44.3)132.5
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.
INARLA
38. online method using public detections
34.7
±13.2
42.1
±9.7
70.7 90 (12.5)216 (30.0)9,855 29,158 52.5 76.6 1.7 1,112 (21.2)2,848 (54.2)2.6
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.
ELP
39. using public detections
25.0
±0.0
26.2
±0.0
71.2 54 (7.5)316 (43.8)7,345 37,344 39.2 76.6 1.3 1,396 (35.6)1,804 (46.0)5.7
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.
JPDA_m
40. using public detections
23.8
±0.0
33.8
±0.0
68.2 36 (5.0)419 (58.1)6,373 40,084 34.8 77.0 1.1 365 (10.5)869 (25.0)32.6
H. Rezatofighi, A. Milan, Z. Zhang, Q. Shi, A. Dick, I. Reid. Joint Probabilistic Data Association Revisited. In ICCV, 2015.
TrackerMOTAIDF1MOTPMTMLFPFNRecall PrecisionFAFID Sw.FragHz
HAM_INTP15
41. online method using public detections
28.6
±13.8
41.4
±10.0
71.1 72 (10.0)317 (44.0)7,485 35,910 41.6 77.3 1.3 460 (11.1)1,038 (25.0)18.7
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.
CNNTCM
42. using public detections
29.6
±13.9
36.8
±13.4
71.8 81 (11.2)317 (44.0)7,786 34,733 43.5 77.4 1.3 712 (16.4)943 (21.7)1.7
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.
UN_DAM
43. online method using public detections
29.7
±12.3
41.4
±10.2
71.4 66 (9.2)360 (49.9)7,610 35,269 42.6 77.5 1.3 318 (7.5)674 (15.8)20.7
Multi Object Tracking using Deep Structural Cost Minimization in Data Association
TENSOR
44. using public detections
24.3
±13.1
24.1
±7.8
71.6 40 (5.5)336 (46.6)6,644 38,582 37.2 77.5 1.1 1,271 (34.2)1,304 (35.1)24.0
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.
CRFTrack_
45. using public detections
40.0
±14.5
49.6
±14.7
71.9 166 (23.0)206 (28.6)10,295 25,917 57.8 77.5 1.8 658 (11.4)1,508 (26.1)3.2
Jun xiang, Chao Ma, Guohan Xu, Jianhua Hou, End-to-End Learning Deep CRF models for Multi-Object Tracking. In IEEE Transactions on Circuits and Systems for Video Technology, 2020
SAS_MOT15
46. using public detections
22.2
±13.8
27.2
±11.2
71.1 22 (3.1)444 (61.6)5,591 41,531 32.4 78.1 1.0 700 (21.6)1,240 (38.3)8.9
A. Maksai, P. Fua. Eliminating Exposure Bias and Metric Mismatch in Multiple Object Tracking. In CVPR, 2019.
AdTobKF
47. online method using public detections
24.8
±12.4
34.5
±11.9
70.8 29 (4.0)375 (52.0)6,201 39,321 36.0 78.1 1.1 666 (18.5)1,300 (36.1)206.5
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.
HybridDAT
48. online method using public detections
35.0
±13.5
47.7
±9.6
72.6 82 (11.4)304 (42.2)8,455 31,140 49.3 78.2 1.5 358 (7.3)1,267 (25.7)4.6
M. Yang, Y. Jia. A Hybrid Data Association Framework for Robust Online Multi-Object Tracking. In IEEE Transactions on Image Processing, 2016.
LINF1
49. using public detections
24.5
±15.4
34.8
±12.8
71.3 40 (5.5)466 (64.6)5,864 40,207 34.6 78.4 1.0 298 (8.6)744 (21.5)7.5
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.
SLTV15
50. online method using public detections
27.6
±14.4
40.3
±11.5
71.4 52 (7.2)374 (51.9)6,581 37,566 38.9 78.4 1.1 358 (9.2)884 (22.7)20.9
Gwangju Institute of Science and Technology(GIST), Machine Learning and Vision Laboratory
TrackerMOTAIDF1MOTPMTMLFPFNRecall PrecisionFAFID Sw.FragHz
ISE_MOT15R
51. online method using public detections
46.7
±16.1
51.6
±9.9
77.1 212 (29.4)185 (25.7)11,003 20,839 66.1 78.7 1.9 878 (13.3)1,265 (19.1)6.7
MIFT
QuadMOT
52. using public detections
33.8
±13.8
40.4
±9.2
73.4 93 (12.9)266 (36.9)7,898 32,061 47.8 78.8 1.4 703 (14.7)1,430 (29.9)3.7
J. Son, M. Baek, M. Cho, B. Han. Multi-Object Tracking with Quadruplet Convolutional Neural Networks. In CVPR, 2017.
NOMT
53. using public detections
33.7
±16.2
44.6
±14.3
71.9 88 (12.2)317 (44.0)7,762 32,547 47.0 78.8 1.3 442 (9.4)823 (17.5)11.5
W. Choi. Near-Online Multi-target Tracking with Aggregated Local Flow Descriptor. In ICCV, 2015.
TSMLCDEnew
54. using public detections
34.3
±13.1
44.1
±11.9
71.7 101 (14.0)284 (39.4)7,869 31,908 48.1 79.0 1.4 618 (12.9)959 (20.0)6.5
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.
TLO15
55. online method using public detections
40.0
±14.9
44.3
±9.9
73.4 123 (17.1)208 (28.8)9,349 26,328 57.1 79.0 1.6 1,207 (21.1)1,624 (28.4)24.6
Anonymous submission
PHD_GSDL
56. online method using public detections
30.5
±14.2
38.8
±10.0
71.2 55 (7.6)297 (41.2)6,534 35,284 42.6 80.0 1.1 879 (20.6)2,208 (51.9)8.2
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.
AMIR15
57. online method using public detections
37.6
±12.6
46.0
±7.9
71.7 114 (15.8)193 (26.8)7,933 29,397 52.2 80.2 1.4 1,026 (19.7)2,024 (38.8)1.9
A. Sadeghian, A. Alahi, S. Savarese. Tracking The Untrackable: Learning To Track Multiple Cues with Long-Term Dependencies. In ICCV, 2017.
SCEA
58. online method using public detections
29.1
±12.2
37.2
±10.3
71.1 64 (8.9)341 (47.3)6,060 36,912 39.9 80.2 1.0 604 (15.1)1,182 (29.6)6.8
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.
GMPHD_OGM
59. online method using public detections
30.7
±12.6
38.8
±9.3
71.6 83 (11.5)275 (38.1)6,502 35,030 43.0 80.2 1.1 1,034 (24.1)1,351 (31.4)169.5
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.
TBSS15
60. online method using public detections
29.2
±12.4
37.2
±9.0
71.3 49 (6.8)316 (43.8)6,068 36,779 40.1 80.3 1.1 649 (16.2)1,508 (37.6)11.5
X. Zhou, P. Jiang, Z. Wei, H. Dong, F. Wang. Online Multi-Object Tracking with Structural Invariance Constraint. In BMVC, 2018.
TrackerMOTAIDF1MOTPMTMLFPFNRecall PrecisionFAFID Sw.FragHz
RAR15pub
61. online method using public detections
35.1
±12.4
45.4
±11.4
70.9 94 (13.0)305 (42.3)6,771 32,717 46.7 80.9 1.2 381 (8.1)1,523 (32.6)5.4
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.
TLO
62. using public detections
41.3
±0.0
46.1
±0.0
73.5 113 (15.7)249 (34.5)8,000 27,210 55.7 81.1 1.4 852 (15.3)1,405 (25.2)5.0
Anonymous submission
TFMOT
63. online method using public detections
23.8
±12.0
32.3
±11.7
71.3 35 (4.9)447 (62.0)4,533 41,873 31.8 81.2 0.8 404 (12.7)792 (24.9)11.3
M. Abhijeet Boragule. Joint Cost Minimization for Multi-Object Tracking. In 2017 IEEE International Conference on Advanced Vide and Signale Based Surveillance, 2017.
KCF
64. online method using public detections
38.9
±13.9
44.5
±11.2
70.6 120 (16.6)227 (31.5)7,321 29,501 52.0 81.4 1.3 720 (13.9)1,440 (27.7)0.3
P. Chu, H. Fan, C. Tan, H. Ling. Online Multi-Object Tracking with Instance-Aware Tracker and Dynamic Model Refreshment. In WACV, 2019.
TO
65. using public detections
25.7
±13.5
32.7
±11.3
72.2 31 (4.3)414 (57.4)4,779 40,511 34.1 81.4 0.8 383 (11.2)600 (17.6)5.0
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.
LFNF
66. using public detections
31.6
±13.2
33.1
±8.3
72.0 69 (9.6)301 (41.7)5,943 35,095 42.9 81.6 1.0 961 (22.4)1,106 (25.8)4.0
Sheng H, Hao L, Chen J, et al. Robust Local Effective Matching Model for Multi-Target Tracking. In PCM, 2017
TARCA
67. online method using public detections
48.7
±12.8
58.4
±12.1
74.1 211 (29.3)169 (23.4)8,855 22,110 64.0 81.6 1.5 567 (8.9)1,147 (17.9)5.9
Anonymous submission
SiameseCNN
68. using public detections
29.0
±15.1
34.3
±14.4
71.2 61 (8.5)349 (48.4)5,160 37,798 38.5 82.1 0.9 639 (16.6)1,316 (34.2)52.8
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.
DCCRF
69. online method using public detections
33.6
±10.9
39.1
±10.1
70.9 75 (10.4)271 (37.6)5,917 34,002 44.7 82.3 1.0 866 (19.4)1,566 (35.1)0.1
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
70. online method using public detections
35.1
±12.9
47.5
±11.5
71.5 89 (12.3)276 (38.3)5,678 33,815 45.0 83.0 1.0 383 (8.5)1,175 (26.1)1.0
Yuan Zhang, Di Xie and Shiliang Pu (Hikvision Research Institute)
TrackerMOTAIDF1MOTPMTMLFPFNRecall PrecisionFAFID Sw.FragHz
AM
71. online method using public detections
34.3
±13.1
48.3
±12.2
70.5 82 (11.4)313 (43.4)5,154 34,848 43.3 83.8 0.9 348 (8.0)1,463 (33.8)0.5
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.
CDA_DDALpb
72. online method using public detections
32.8
±10.6
38.8
±8.0
70.7 70 (9.7)304 (42.2)4,983 35,690 41.9 83.8 0.9 614 (14.7)1,583 (37.8)2.3
S. Bae and K. Yoon, Confidence-Based Data Association and Discriminative Deep Appearance Learning for Robust Online Multi-Object Tracking , In IEEE TPAMI, 2017.
MPNTrack
73. using public detections
51.5
±10.8
58.6
±7.5
76.0 225 (31.2)187 (25.9)7,620 21,780 64.6 83.9 1.3 375 (5.8)872 (13.5)6.5
G. Brasó, L. Leal-Taixé. Learning a Neural Solver for Multiple Object Tracking. In CVPR, 2020.
Tracktor++
74. online method using public detections
44.1
±11.7
46.7
±10.3
75.0 130 (18.0)189 (26.2)6,477 26,577 56.7 84.3 1.1 1,318 (23.2)1,790 (31.5)0.9
P. Bergmann, T. Meinhardt, L. Leal-Taixé. Tracking without bells and whistles. In ICCV, 2019.
STRN
75. online method using public detections
38.1
±11.3
46.6
±7.8
72.1 83 (11.5)241 (33.4)5,451 31,571 48.6 84.6 0.9 1,033 (21.2)2,665 (54.8)13.8
J. Xu, Y. Cao, Z. Zhang, H. Hu. Spatial-Temporal Relation Networks for Multi-Object Tracking. In ICCV, 2019.
TrctrD15
76. online method using public detections
44.1
±11.7
46.0
±10.6
75.3 124 (17.2)192 (26.6)6,085 26,917 56.2 85.0 1.1 1,347 (24.0)1,868 (33.2)1.6
Y. Xu, A. Osep, Y. Ban, R. Horaud, L. Leal-Taixe, X. Alameda-Pineda. How To Train Your Deep Multi-Object Tracker. In , 2019.
Lif_T
77. using public detections
52.5
±0.0
60.0
±0.0
76.3 244 (33.8)186 (25.8)6,837 21,610 64.8 85.3 1.2 730 (11.3)1,047 (16.2)1.5
Anonymous submission
MANET
78. online method using public detections
47.4
±10.5
49.5
±9.7
76.1 173 (24.0)193 (26.8)6,044 25,164 59.0 85.7 1.0 1,087 (18.4)1,290 (21.8)11.9
Anonymous submission
AP_HWDPL_p
79. online method using public detections
38.5
±8.8
47.1
±9.3
72.6 63 (8.7)270 (37.4)4,005 33,203 46.0 87.6 0.7 586 (12.8)1,263 (27.5)6.7
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.
Tracktor++v2
80. online method using public detections
46.6
±10.0
47.6
±10.2
76.4 131 (18.2)201 (27.9)4,624 26,896 56.2 88.2 0.8 1,290 (22.9)1,702 (30.3)1.4
P. Bergmann, T. Meinhardt, L. Leal-Taixé. Tracking without bells and whistles. In ICCV, 2019.
SequencesFramesTrajectoriesBoxes
11578372161440

Difficulty Analysis

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

TUD-Crossing

TUD-Crossing

(64.3 MOTA)

PETS09-S2L2

PETS09-S2L2

(43.6 MOTA)

ETH-Jelmoli

ETH-Jelmoli

(40.6 MOTA)

...

...

Venice-1

Venice-1

(24.0 MOTA)

ADL-Rundle-1

ADL-Rundle-1

(18.0 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. 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.
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.
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.