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

TrackerMOTAIDF1MOTPMTMLFPFNRecallPrecisionFAFID Sw.FragHz
UN_DAM
1. 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
TSMLCDEnew
2. 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.
TSDA_OAL
3. 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.
TrctrD15
4. 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.
Tracktor++v2
5. 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.
Tracktor++
6. 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.
Tracker
7. 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
TO
8. 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.
TLO
9. 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
TLO15
10. 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
TrackerMOTAIDF1MOTPMTMLFPFNRecallPrecisionFAFID Sw.FragHz
TFMOT
11. 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.
TENSOR
12. 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.
TDAM
13. 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.
TC_SIAMESE
14. 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.
TC_ODAL
15. 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.
TBX
16. 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.
TBSS15
17. 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.
TBD
18. 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.
STRN
19. 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.
SST_MOT15
20. online method using public detections
35.8
±10.0
39.6
±8.7
72.4 56 (7.8)281 (39.0)4,065 33,669 45.2 87.2 0.7 1,728 (38.2)1,312 (29.0)6.3
Shijie Sun, Naveed Akhtar, Ajmal Mian
TrackerMOTAIDF1MOTPMTMLFPFNRecallPrecisionFAFID Sw.FragHz
SMOT
21. 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.
SLTV15
22. 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
SiameseCNN
23. 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.
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.
sc_tracker
25. online method using public detections
14.8
±8.5
18.5
±5.0
72.1 11 (1.5)440 (61.0)6,595 43,804 28.7 72.8 1.1 1,962 (68.4)2,288 (79.7)11.7
Anonymous submission
SCEA
26. 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.
SC2D
27. 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
SAS_MOT15
28. 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.
RSCNN
29. 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.
RNN_LSTM
30. 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.
TrackerMOTAIDF1MOTPMTMLFPFNRecallPrecisionFAFID Sw.FragHz
RMOT
31. 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.
RAT
32. 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
RAR15pub
33. 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.
QuadMOT
34. 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.
PHD_GSDL
35. 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.
OMT_DFH
36. 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.
oICF
37. 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.
NOMT
38. 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.
MTSTracker
39. 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.
MPNTrack
40. 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.
TrackerMOTAIDF1MOTPMTMLFPFNRecallPrecisionFAFID Sw.FragHz
MotiCon
41. 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.
mLK
42. 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)
MIFTv2
43. online method using public detections
48.1
±14.1
52.1
±9.0
77.1 213 (29.5)189 (26.2)10,246 20,840 66.1 79.8 1.8 776 (11.7)1,197 (18.1)6.7
MHT_DAM
44. 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.
MDP
45. 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.
MCF_PHD
46. 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.
MANET
47. 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
LT2015
48. online method using public detections
31.5
±14.8
37.7
±9.4
70.2 90 (12.5)253 (35.1)8,383 32,727 46.7 77.4 1.5 950 (20.3)2,675 (57.2)14.2
Anonymous submission
LP_SSVM
49. 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.
LP2D
50. 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.
TrackerMOTAIDF1MOTPMTMLFPFNRecallPrecisionFAFID Sw.FragHz
LINF1
51. 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.
Lif_T
52. 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
A. Hornakova, R. Henschel, B. Rosenhahn, P. Swoboda. Lifted Disjoint Paths with Application in Multiple Object Tracking. In ICML, 2020.
Lif_TsimInt
53. using public detections
47.2
±15.8
57.6
±14.7
75.9 195 (27.0)215 (29.8)7,635 24,277 60.5 83.0 1.3 554 (9.2)803 (13.3)5.8
A. Hornakova, R. Henschel, B. Rosenhahn, P. Swoboda. Lifted Disjoint Paths with Application in Multiple Object Tracking. In ICML, 2020.
LFNF
54. 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
LDCT
55. 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
KCF
56. 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.
JPDA_m
57. 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.
JointMC
58. 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.
ISE_MOT15R
59. 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
INARLA
60. 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.
TrackerMOTAIDF1MOTPMTMLFPFNRecallPrecisionFAFID Sw.FragHz
HybridDAT
61. 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.
HAM_SADF
62. 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.
HAM_INTP15
63. 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.
GSCR
64. 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.
GMPHD_OGM
65. 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.
GMPHD
66. 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.
GMMA_intp
67. 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.
FC15S2
68. online method using public detections
34.0
±14.1
46.7
±9.1
71.5 105 (14.6)272 (37.7)8,376 31,680 48.4 78.0 1.4 474 (9.8)1,524 (31.5)17.1
Anonymous submission
ELP
69. 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.
EDA_GNN
70. 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
TrackerMOTAIDF1MOTPMTMLFPFNRecallPrecisionFAFID Sw.FragHz
EAMTTpub
71. 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
DP_NMS
72. 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.
DEEPDA_MOT
73. 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.
DCO_X
74. 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.
DCCRF
75. 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.
CRFTrack_
76. 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
CppSORT
77. 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.
CNNTCM
78. 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.
CEM
79. 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.
CDA_DDALpb
80. 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.
TrackerMOTAIDF1MOTPMTMLFPFNRecallPrecisionFAFID Sw.FragHz
AP_HWDPL_p
81. 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.
AMIR15
82. 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.
AM
83. 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.
ALExTRAC
84. 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.
AdTobKF
85. 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.
SequencesFramesTrajectoriesBoxes
11578372161440

Difficulty Analysis

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

TUD-Crossing

TUD-Crossing

(64.4 MOTA)

PETS09-S2L2

PETS09-S2L2

(43.8 MOTA)

ETH-Jelmoli

ETH-Jelmoli

(40.9 MOTA)

...

...

Venice-1

Venice-1

(24.2 MOTA)

ADL-Rundle-1

ADL-Rundle-1

(18.2 MOTA)


Evaluation Measures

Lower is better. Higher is better.
Measure Better Perfect Description
MOTA higher 100%Multi-Object Tracking Accuracy (+/- denotes standard deviation across all sequences) [1]. This measure combines three error sources: false positives, missed targets and identity switches.
IDF1 higher 100%ID F1 Score [2]. The ratio of correctly identified detections over the average number of ground-truth and computed detections.
MOTP higher 100%Multi-Object Tracking Precision (+/- denotes standard deviation across all sequences) [1]. The misalignment between the annotated and the predicted bounding boxes.
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 0The total number of false positives.
FN lower 0The total number of false negatives (missed targets).
Recall higher 100%Ratio of correct detections to total number of GT boxes.
Precision higher 100%Ratio of TP / (TP+FP).
FAF lower 0The average number of false alarms per frame.
ID Sw. lower 0Number of Identity Switches (ID switch ratio = #ID switches / recall) [3]. Please note that we follow the stricter definition of identity switches as described in the reference
Frag lower 0The 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 private detections This method used a private 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.