MOT16 Results

Download all results for this benchmark

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

TrackerMOTAIDF1HOTAMTMLFPFNRcllPrcnAssADetAAssReAssPrDetReDetPrLocAFAFID Sw.FragHz
GMOTv2
1. using public detections
76.6
±10.8
75.0 60.9 395 (52.0)107 (14.1)12,918 29,120 84.0 92.2 59.3 62.8 66.1 74.5 69.7 76.5 83.3 2.2 568 (0.0)821 (0.0)4.0
VAITracker2
2. using public detections
76.5
±12.2
74.7 61.3 380 (50.1)107 (14.1)13,528 28,193 84.5 91.9 60.3 62.6 66.6 75.9 69.7 75.8 83.0 2.3 1,062 (0.0)1,304 (0.0)45.2
man_kcart
3. using public detections
76.1
±12.3
74.9 61.2 380 (50.1)110 (14.5)13,559 29,088 84.0 91.9 60.5 62.3 67.1 75.7 69.5 75.9 83.1 2.3 920 (0.0)1,316 (0.0)22.3
SacMOT
4. using public detections
75.8
±12.5
74.6 61.0 337 (44.4)133 (17.5)9,574 33,987 81.4 93.9 60.0 62.3 66.7 75.7 67.9 78.4 83.7 1.6 609 (0.0)1,359 (0.0)18.7
MrMOT
5. online method using public detections
75.0
±12.9
73.9 60.1 330 (43.5)129 (17.0)8,743 35,858 80.3 94.4 58.5 61.9 64.6 76.1 67.0 78.7 83.6 1.5 1,009 (0.0)2,473 (0.0)22.3
RLMOT
6. using public detections
74.7
±13.2
73.8 60.0 322 (42.4)131 (17.3)8,409 36,757 79.8 94.5 58.6 61.6 64.5 76.4 66.6 78.9 83.7 1.4 963 (12.1)2,587 (32.4)3.3
GOT
7. online method using public detections
74.7
±13.3
71.8 59.1 299 (39.4)144 (19.0)7,635 37,892 79.2 95.0 57.3 61.1 63.4 75.7 65.8 78.9 83.5 1.3 683 (8.6)2,409 (30.4)14.8
xumot
8. using public detections
74.4
±13.3
73.7 60.2 350 (46.1)115 (15.2)11,693 33,763 81.5 92.7 58.9 61.7 64.4 76.9 67.9 77.2 83.5 2.0 1,158 (0.0)2,484 (0.0)15.7
InvoMOT
9. online method using public detections
71.8
±15.2
74.4 59.3 269 (35.4)185 (24.4)7,692 42,851 76.5 94.8 60.5 58.4 65.0 80.1 62.9 77.9 82.8 1.3 826 (0.0)2,671 (0.0)19.7
NEUT
10. online method using public detections
71.6
±15.2
72.6 59.1 253 (33.3)194 (25.6)3,514 47,536 73.9 97.5 59.5 58.9 64.3 79.8 62.0 81.8 84.3 0.6 671 (0.0)2,966 (0.0)22.3
TrackerMOTAIDF1HOTAMTMLFPFNRcllPrcnAssADetAAssReAssPrDetReDetPrLocAFAFID Sw.FragHz
vc_tracker
11. online method using public detections
68.2
±14.7
70.8 57.7 214 (28.2)240 (31.6)2,698 54,834 69.9 97.9 59.4 56.2 62.8 81.8 58.8 82.4 84.4 0.5 470 (6.7)1,300 (18.6)22.7
MAT
12. online method using public detections
67.7
±10.5
69.6 56.3 288 (37.9)202 (26.6)6,337 52,234 71.4 95.4 57.0 55.7 62.6 77.0 59.4 79.4 83.2 1.1 379 (5.3)623 (8.7)11.5
MAT: Motion-Aware Multi-Object Tracking
UIUCTracker
13. using public detections
67.6
±14.3
57.4 49.2 272 (35.8)150 (19.8)12,456 44,699 75.5 91.7 44.8 54.7 47.6 79.5 60.4 73.4 80.9 2.1 1,983 (26.3)2,218 (29.4)7.7
CNT
14. online method using public detections
65.2
±10.8
59.1 48.1 238 (31.4)133 (17.5)8,600 52,978 70.9 93.8 43.7 53.3 50.6 66.1 57.5 76.1 81.7 1.5 1,926 (27.1)3,706 (52.2)11.1
AOST
15. using public detections
64.8
±9.9
66.7 53.8 277 (36.5)149 (19.6)15,084 47,941 73.7 89.9 52.6 55.4 58.9 72.2 60.9 74.3 81.1 2.5 1,092 (14.8)2,644 (35.9)23.6
hugmot
16. online method using public detections
64.5
±11.7
62.8 49.1 216 (28.5)208 (27.4)5,344 58,625 67.8 95.9 48.0 50.7 53.5 71.1 54.0 76.3 80.8 0.9 685 (10.1)3,582 (52.8)30.8
Multiple Object Tracking by Tracjectory Map Regression with Temporal Priors Embedding
XJTU
17. online method using public detections
64.4
±11.7
63.0 49.3 220 (29.0)204 (26.9)5,550 58,554 67.9 95.7 48.3 50.8 53.7 71.6 54.1 76.2 80.8 0.9 728 (10.7)3,608 (53.1)29.6
Multiple Object Tracking by Trajectory Map Regression
TMOH
18. online method using public detections
63.2
±11.4
63.5 50.7 205 (27.0)235 (31.0)3,122 63,376 65.2 97.4 50.7 51.0 54.6 77.5 53.5 79.9 82.6 0.5 635 (9.7)1,486 (22.8)0.7
D. Stadler, J. Beyerer. Improving Multiple Pedestrian Tracking by Track Management and Occlusion Handling. In CVPR, 2021.
ArTIST_C
19. online method using public detections
63.0
±14.5
61.9 49.8 221 (29.1)252 (33.2)7,420 59,376 67.4 94.3 49.5 50.6 53.6 75.4 54.2 75.8 81.0 1.3 635 (9.4)927 (13.7)4.5
MPTC
20. online method using public detections
62.9
±11.6
65.1 51.1 195 (25.7)240 (31.6)3,361 63,565 65.1 97.2 51.5 50.9 55.7 75.4 53.5 79.8 82.7 0.6 685 (10.5)1,716 (26.3)1.2
D. Stadler, J. Beyerer. Multi-Pedestrian Tracking with Clusters. In 2021 17th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), 2021.
TrackerMOTAIDF1HOTAMTMLFPFNRcllPrcnAssADetAAssReAssPrDetReDetPrLocAFAFID Sw.FragHz
STTA
21. online method using public detections
62.9
±11.7
67.9 52.7 209 (27.5)241 (31.8)4,646 62,285 65.8 96.3 55.5 50.3 60.7 75.6 53.2 77.8 81.7 0.8 750 (0.0)957 (0.0)3.2
UnsupTrack
22. online method using public detections
62.4
±14.6
58.5 47.0 205 (27.0)242 (31.9)5,909 61,981 66.0 95.3 44.8 49.8 53.8 64.1 53.0 76.5 81.2 1.0 588 (8.9)1,361 (20.6)1.9
S. Karthik, A. Prabhu, V. Gandhi. Simple Unsupervised Multi-Object Tracking. In Arxiv, 2020.
SSL_MOT
23. online method using public detections
62.1
±11.6
45.2 40.6 208 (27.4)222 (29.2)4,760 63,238 65.3 96.2 32.9 50.5 49.9 43.0 53.5 78.8 82.4 0.8 1,022 (0.0)1,356 (0.0)13.7
ApLift
24. using public detections
61.7
±11.0
66.1 51.3 260 (34.3)237 (31.2)9,168 60,180 67.0 93.0 53.2 49.8 59.2 73.1 53.8 74.6 80.7 1.5 495 (0.0)802 (0.0)0.6
A. Hornakova*, T. Kaiser*, M. Rolinek, B. Rosenhahn, P. Swoboda, R. Henschel. Making Higher Order MOT Scalable: An Efficient Approximate Solver for Lifted Disjoint Paths. In International Conference on Computer Vision (ICCV), 2021.
Lif_T
25. using public detections
61.3
±0.0
64.7 50.8 205 (27.0)258 (34.0)4,844 65,401 64.1 96.0 53.1 48.9 57.2 78.9 51.7 77.4 81.4 0.8 389 (6.1)1,034 (16.1)0.5
A. Hornakova, R. Henschel, B. Rosenhahn, P. Swoboda. Lifted Disjoint Paths with Application in Multiple Object Tracking. In ICML, 2020.
SORT_OH
26. online method using public detections
61.1
±10.8
61.0 49.4 240 (31.6)162 (21.3)12,289 57,742 68.3 91.0 47.5 51.6 53.4 71.1 56.3 75.0 81.4 2.1 850 (0.0)1,332 (0.0)162.6
ITM
27. online method using public detections
61.1
±12.6
59.5 0.0 213 (28.1)194 (25.6)8,919 61,019 66.5 93.2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.5 1,052 (15.8)1,933 (29.1)1.1
SLA_public
28. online method using public detections
60.6
±9.9
59.5 46.8 184 (24.2)221 (29.1)5,783 65,469 64.1 95.3 45.3 48.8 52.9 66.7 51.6 76.7 80.7 1.0 643 (10.0)1,171 (18.3)12.9
Spatial-Attention Location-Aware Multi-Object Tracking. In , 2020.
ISE_MOT16
29. online method using public detections
60.1
±9.2
56.9 45.2 198 (26.1)221 (29.1)6,964 65,044 64.3 94.4 42.6 48.3 46.5 72.0 51.5 75.6 80.5 1.2 739 (11.5)951 (14.8)6.9
MIFT
mfi_tst
30. online method using public detections
59.9
±10.2
58.7 46.9 183 (24.1)234 (30.8)3,660 68,923 62.2 96.9 46.0 48.1 49.0 77.5 50.5 78.6 81.8 0.6 616 (0.0)1,050 (0.0)0.7
J. Y, H. Ge, J. Yang, Y. Tong, S. Su. Online multi-object tracking using multi-function integration and tracking simulation training. In Applied Intelligence, 2021.
TrackerMOTAIDF1HOTAMTMLFPFNRcllPrcnAssADetAAssReAssPrDetReDetPrLocAFAFID Sw.FragHz
IFA_MOT
31. online method using public detections
59.8
±9.4
48.0 40.4 199 (26.2)144 (19.0)9,118 61,617 66.2 93.0 33.3 49.5 35.9 74.7 53.5 75.2 81.2 1.5 2,599 (39.3)4,490 (67.8)1.8
LPC_MOT
32. using public detections
58.8
±10.3
67.6 51.7 207 (27.3)266 (35.0)6,167 68,432 62.5 94.9 56.4 47.6 60.2 80.1 50.5 76.7 81.3 1.0 435 (7.0)628 (10.1)4.3
P. Dai, R. Weng, W. Choi, C. Zhang, Z. He, W. Ding. Learning a Proposal Classifier for Multiple Object tracking. In CVPR (Accepted), 2021.
GCNoMOT
33. online method using public detections
58.8
±10.7
43.1 37.0 265 (34.9)140 (18.4)14,127 58,528 67.9 89.8 27.3 50.8 37.6 45.4 55.9 73.9 81.4 2.4 2,402 (0.0)2,324 (0.0)7.8
MPNTrack
34. using public detections
58.6
±10.3
61.7 48.9 207 (27.3)258 (34.0)4,949 70,252 61.5 95.8 51.1 47.1 56.8 74.9 49.8 77.6 81.7 0.8 354 (5.8)684 (11.1)6.5
G. Brasó, L. Leal-Taixé. Learning a Neural Solver for Multiple Object Tracking. In CVPR, 2020.
Seed_MOT
35. using public detections
57.7
±10.5
66.1 53.8 318 (41.9)148 (19.5)36,735 39,281 78.5 79.6 54.2 53.7 60.7 72.9 65.0 65.9 80.1 6.2 1,099 (14.0)1,674 (21.3)591.9
Lif_TsimInt
36. using public detections
57.5
±9.2
64.1 49.6 193 (25.4)263 (34.7)4,249 72,868 60.0 96.3 53.3 46.5 57.4 79.3 48.9 78.3 81.9 0.7 335 (5.6)604 (10.1)5.9
A. Hornakova, R. Henschel, B. Rosenhahn, P. Swoboda. Lifted Disjoint Paths with Application in Multiple Object Tracking. In ICML, 2020.
GNNMatch
37. online method using public detections
57.2
±11.0
55.0 44.6 174 (22.9)258 (34.0)3,905 73,493 59.7 96.5 43.7 45.8 51.0 68.1 48.2 78.0 81.7 0.7 559 (9.4)847 (14.2)0.3
I. Papakis, A. Sarkar, A. Karpatne. GCNNMatch: Graph Convolutional Neural Networks for Multi-Object Tracking via Sinkhorn Normalization. In arXiv preprint arXiv:2010.00067, 2020.
GSM_Tracktor
38. online method using public detections
57.0
±10.7
58.2 45.9 167 (22.0)262 (34.5)4,332 73,573 59.6 96.2 46.7 45.4 50.1 77.5 47.8 77.1 81.1 0.7 475 (8.0)859 (14.4)7.6
Q. Liu, Q. Chu, B. Liu, N. Yu. Gsm: graph similarity model for multi-object tracking. In International Joint Conferences on Artificial Intelligence Organization, 2020.
FGRNetIV
39. online method using public detections
56.5
±11.3
55.4 44.8 156 (20.6)269 (35.4)2,736 75,922 58.4 97.5 44.7 45.1 47.6 79.0 47.2 78.9 81.9 0.5 611 (10.5)1,035 (17.7)1.5
Tracktor++v2
40. online method using public detections
56.2
±11.4
54.9 44.6 157 (20.7)272 (35.8)2,394 76,844 57.9 97.8 44.6 44.8 47.6 79.3 46.8 79.1 82.0 0.4 617 (10.7)1,068 (18.5)1.6
P. Bergmann, T. Meinhardt, L. Leal-Taixé. Tracking without bells and whistles. In ICCV, 2019.
TrackerMOTAIDF1HOTAMTMLFPFNRcllPrcnAssADetAAssReAssPrDetReDetPrLocAFAFID Sw.FragHz
TrctrD16
41. online method using public detections
54.8
±11.8
53.4 42.2 145 (19.1)281 (37.0)2,955 78,765 56.8 97.2 41.6 43.3 45.7 73.6 45.2 77.4 80.6 0.5 645 (11.4)1,515 (26.7)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.
VAN_on
42. online method using public detections
54.6
±10.9
54.2 43.5 147 (19.4)275 (36.2)2,307 79,895 56.2 97.8 43.8 43.6 47.4 75.3 45.5 79.2 82.1 0.4 619 (11.0)1,574 (28.0)8.1
Tracktor++
43. online method using public detections
54.4
±12.0
52.5 42.3 144 (19.0)280 (36.9)3,280 79,149 56.6 96.9 41.4 43.5 45.0 75.9 45.5 77.9 81.1 0.6 682 (12.1)1,480 (26.2)1.5
P. Bergmann, T. Meinhardt, L. Leal-Taixé. Tracking without bells and whistles. In ICCV, 2019.
HDTR
44. using public detections
53.6
±8.7
46.6 46.8 161 (21.2)281 (37.0)4,714 79,353 56.5 95.6 49.4 44.5 54.3 75.6 46.8 79.2 83.0 0.8 618 (10.9)833 (14.7)3.6
M. Babaee, A. Athar, G. Rigoll. Multiple People Tracking Using Hierarchical Deep Tracklet Re-identification. In arXiv preprint arXiv:1811.04091, 2018.
TPM
45. using public detections
51.3
±9.0
47.9 36.7 142 (18.7)310 (40.8)2,701 85,504 53.1 97.3 34.6 39.3 37.7 70.4 41.0 75.0 79.1 0.5 569 (10.7)707 (13.3)0.8
J. Peng, T. Wang, et.al. TPM: Multiple Object Tracking with Tracklet-Plane Matching. In Pattern Recognition, 2020.
RFS
46. online method using public detections
50.9
±11.8
53.9 40.5 127 (16.7)298 (39.3)8,884 79,918 56.2 92.0 41.1 40.4 44.2 71.7 43.1 70.7 77.6 1.5 714 (12.7)1,799 (32.0)1.0
MTSFS：Online Multi-Object Tracking Based on Salient Feature Selection in Crowded Scenes
HOMI_Tracker
47. online method using public detections
50.4
±12.6
47.5 40.4 170 (22.4)232 (30.6)18,730 69,800 61.7 85.7 36.3 45.4 39.2 75.8 50.7 70.4 80.1 3.2 1,826 (29.6)3,214 (52.1)9.9
PV
48. online method using public detections
50.4
±10.3
50.8 39.4 113 (14.9)295 (38.9)2,600 86,780 52.4 97.4 39.1 40.0 43.0 68.1 41.6 77.3 80.6 0.4 1,061 (20.2)3,181 (60.7)7.3
X. S. Li, Y. T. Liu, K. F. Wang. Multi-Target Tracking with Trajectory Prediction and Re-Identification//2019 Chinese Automation Congress. IEEE.
CRF_TRACK
49. using public detections
50.3
±7.9
54.4 40.7 139 (18.3)271 (35.7)7,148 82,746 54.6 93.3 41.6 40.1 44.2 74.6 42.5 72.7 78.5 1.2 702 (12.9)1,387 (25.4)1.5
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
ENFT16
50. using public detections
50.3
±8.2
55.0 41.7 146 (19.2)302 (39.8)8,341 81,843 55.1 92.3 42.8 40.9 46.7 74.0 43.6 73.1 79.4 1.4 490 (8.9)754 (13.7)0.4
BUAA
TrackerMOTAIDF1HOTAMTMLFPFNRcllPrcnAssADetAAssReAssPrDetReDetPrLocAFAFID Sw.FragHz
CMT16
51. using public detections
49.8
±9.0
59.2 44.4 126 (16.6)331 (43.6)9,229 81,882 55.1 91.6 48.7 40.7 52.5 75.9 43.6 72.5 79.3 1.6 365 (6.6)617 (11.2)6.3
#Submission: TIP-21190-2019
NOTA
52. using public detections
49.8
±8.3
55.3 40.7 136 (17.9)286 (37.7)7,248 83,614 54.1 93.2 42.0 39.7 45.7 72.6 42.0 72.3 78.2 1.2 614 (11.3)1,372 (25.3)19.2
L. Chen, H. Ai, R. Chen, Z. Zhuang. Aggregate Tracklet Appearance Features for Multi-Object Tracking. In IEEE Signal Processing Letters, 2019.
siameseCos
53. using public detections
49.4
±8.1
49.8 38.3 145 (19.1)299 (39.4)6,281 85,384 53.2 93.9 37.4 39.5 40.1 75.4 41.8 73.8 79.4 1.1 679 (12.8)823 (15.5)0.8
In preparation
HCC
54. using public detections
49.3
±10.2
50.7 39.9 135 (17.8)303 (39.9)5,333 86,795 52.4 94.7 39.7 40.4 49.2 62.1 42.6 77.0 81.8 0.9 391 (7.5)535 (10.2)0.8
L. Ma, S. Tang, M. Black, L. Gool. Customized Multi-Person Tracker. In Computer Vision -- ACCV 2018, 2018.
LSST16O
55. online method using public detections
49.2
±10.2
56.5 41.5 102 (13.4)314 (41.4)7,187 84,875 53.4 93.1 44.3 39.2 48.1 71.8 41.4 72.2 77.9 1.2 606 (11.3)2,497 (46.7)2.0
Multi-Object Tracking with Multiple Cues and Switcher-Aware Classification
eTC
56. using public detections
49.2
±9.1
56.1 42.0 131 (17.3)306 (40.3)8,400 83,702 54.1 92.2 44.5 39.9 48.4 72.7 42.6 72.5 78.8 1.4 606 (11.2)882 (16.3)0.7
G. Wang, Y. Wang, H. Zhang, R. Gu, J. Hwang. Exploit the connectivity: Multi-object tracking with trackletnet. In Proceedings of the 27th ACM International Conference on Multimedia, 2019.
AFN
57. using public detections
49.0
±10.2
48.2 38.6 145 (19.1)271 (35.7)9,508 82,506 54.7 91.3 36.5 41.1 39.9 74.2 44.2 73.7 80.8 1.6 899 (16.4)1,383 (25.3)0.6
H. Shen, L. Huang, C. Huang, W. Xu. Tracklet Association Tracker: An End-to-End Learning-based Association Approach for Multi-Object Tracking. In CoRR, 2018.
KCF16
58. online method using public detections
48.8
±9.6
47.2 37.2 120 (15.8)289 (38.1)5,875 86,567 52.5 94.2 35.9 39.1 37.8 77.0 41.2 73.9 79.0 1.0 906 (17.3)1,116 (21.2)0.1
P. Chu, H. Fan, C. Tan, H. Ling. Online Multi-Object Tracking with Instance-Aware Tracker and Dynamic Model Refreshment. In WACV, 2019.
LMP
59. using public detections
48.8
±8.9
51.3 41.0 138 (18.2)304 (40.1)6,654 86,245 52.7 93.5 41.8 40.5 43.8 81.0 43.0 76.2 81.8 1.1 481 (9.1)595 (11.3)0.5
S. Tang, M. Andriluka, B. Andres, B. Schiele. Multiple People Tracking with Lifted Multicut and Person Re-identification. In CVPR, 2017.
TLMHT
60. using public detections
48.7
±8.6
55.3 42.0 119 (15.7)338 (44.5)6,632 86,504 52.6 93.5 45.1 39.4 48.2 77.1 41.7 74.2 79.6 1.1 413 (7.9)642 (12.2)4.8
H. Sheng, J. Chen, Y. Zhang, W. Ke, Z. Xiong, J. Yu. Iterative Multiple Hypothesis Tracking with Tracklet-level Association. In IEEE Transactions on Circuits and Systems for Video Technology, 2018.
TrackerMOTAIDF1HOTAMTMLFPFNRcllPrcnAssADetAAssReAssPrDetReDetPrLocAFAFID Sw.FragHz
STRN_MOT16
61. using public detections
48.5
±8.5
53.9 39.7 129 (17.0)265 (34.9)9,038 84,178 53.8 91.6 40.5 39.1 43.7 71.5 41.7 70.9 77.6 1.5 747 (13.9)2,919 (54.2)13.5
J. Xu, Y. Cao, Z. Zhang, H. Hu. Spatial-Temporal Relation Networks for Multi-Object Tracking. In ICCV, 2019.
BLSTM_MTP_O
62. online method using public detections
48.3
±9.3
53.5 39.7 129 (17.0)294 (38.7)9,792 83,707 54.1 91.0 40.4 39.3 43.2 73.2 42.0 70.6 77.7 1.7 735 (0.0)2,349 (0.0)21.0
C. Kim, F. Li, M. Alotaibi, J. Rehg. Discriminative Appearance Modeling with Multi-track Pooling for Real-time Multi-object Tracking. In CVPR (accepted), 2021.
TSN
63. using public detections
48.2
±8.6
45.7 35.5 151 (19.9)295 (38.9)8,447 85,315 53.2 92.0 33.1 38.6 36.2 69.6 41.3 71.3 78.6 1.4 665 (12.5)829 (15.6)0.8
J. Peng, F. Qiu, et.al. Tracklet Siamese Network with Constrained Clustering for Multiple Object Tracking. In VCIP, 2018.
GCRA
64. using public detections
48.2
±8.3
48.6 37.6 98 (12.9)312 (41.1)5,104 88,586 51.4 94.8 36.7 38.9 39.4 74.3 41.0 75.6 80.6 0.9 821 (16.0)1,117 (21.7)2.8
C. Ma, C. Yang, F. Yang, Y. Zhuang, Z. Zhang, H. Jia, X. Xie. Trajectory Factory: Tracklet Cleaving and Re-connection by Deep Siamese Bi-GRU for Multiple Object Tracking. In ICME, 2018.
FWT
65. using public detections
47.8
±10.0
44.3 35.7 145 (19.1)290 (38.2)8,886 85,487 53.1 91.6 32.7 39.1 35.3 70.7 41.8 72.0 78.7 1.5 852 (16.0)1,534 (28.9)0.6
R. Henschel, L. Leal-Taixé, D. Cremers, B. Rosenhahn. Fusion of Head and Full-Body Detectors for Multi-Object Tracking. In Trajnet CVPRW, 2018.
MOTDT
66. online method using public detections
47.6
±8.4
50.9 38.4 115 (15.2)291 (38.3)9,253 85,431 53.1 91.3 38.3 38.8 41.9 68.6 41.5 71.3 78.3 1.6 792 (14.9)1,858 (35.0)20.6
C. Long, A. Haizhou, Z. Zijie, S. Chong. Real-time Multiple People Tracking with Deeply Learned Candidate Selection and Person Re-identification. In ICME, 2018.
NLLMPa
67. using public detections
47.6
±10.6
47.3 38.0 129 (17.0)307 (40.4)5,844 89,093 51.1 94.1 37.2 39.2 39.0 79.1 41.4 76.1 81.3 1.0 629 (12.3)768 (15.0)8.3
E. Levinkov, J. Uhrig, S. Tang, M. Omran, E. Insafutdinov, A. Kirillov, C. Rother, T. Brox, B. Schiele, B. Andres. Joint Graph Decomposition and Node Labeling: Problem, Algorithms, Applications. In CVPR, 2017.
EAGS16
68. using public detections
47.4
±8.6
50.1 39.1 131 (17.3)324 (42.7)8,369 86,931 52.3 91.9 39.6 38.8 41.8 76.3 41.3 72.6 79.3 1.4 575 (11.0)913 (17.5)197.3
H. Sheng, X. Zhang, Y. Zhang, Y. Wu, J. Chen. Enhanced Association with Supervoxels in Multiple Hypothesis Tracking. In IEEE Access, 2018.
JCSTD
69. online method using public detections
47.4
±8.3
41.1 31.7 109 (14.4)276 (36.4)8,076 86,638 52.5 92.2 26.8 37.9 31.8 54.2 40.4 71.0 77.9 1.4 1,266 (24.1)2,697 (51.4)8.8
W. Tian, M. Lauer, L. Chen. Online Multi-Object Tracking Using Joint Domain Information in Traffic Scenarios. In IEEE Transactions on Intelligent Transportation Systems, 2019.
ASTT
70. using public detections
47.2
±9.6
44.3 35.2 124 (16.3)316 (41.6)4,680 90,877 50.2 95.1 33.2 37.6 34.4 79.4 39.4 74.7 79.6 0.8 633 (12.6)814 (16.2)0.5
Yi Tao el al., “Adaptive Spatio-temporal Model Based Multiple Object Tracking Considering a Moving Camera[C]”, International Conference on Universal Village (UV), 2018.
TrackerMOTAIDF1HOTAMTMLFPFNRcllPrcnAssADetAAssReAssPrDetReDetPrLocAFAFID Sw.FragHz
eHAF16
71. using public detections
47.2
±8.7
52.4 40.3 141 (18.6)325 (42.8)12,586 83,107 54.4 88.7 41.7 39.2 46.6 69.6 42.7 69.7 78.9 2.1 542 (10.0)787 (14.5)0.5
H. Sheng, Y. Zhang, J. Chen, Z. Xiong, J. Zhang. Heterogeneous Association Graph Fusion for Target Association in Multiple Object Tracking. In IEEE Transactions on Circuits and Systems for Video Technology, 2018.
AMIR
72. online method using public detections
47.2
±8.2
46.3 35.1 106 (14.0)316 (41.6)2,681 92,856 49.1 97.1 34.0 36.5 38.0 63.7 38.0 75.2 79.3 0.5 774 (15.8)1,675 (34.1)1.0
A. Sadeghian, A. Alahi, S. Savarese. Tracking The Untrackable: Learning To Track Multiple Cues with Long-Term Dependencies. In ICCV, 2017.
MCjoint
73. using public detections
47.1
±10.3
52.3 41.2 155 (20.4)356 (46.9)6,703 89,368 51.0 93.3 44.7 38.2 47.4 75.9 40.4 73.8 79.5 1.1 370 (7.3)598 (11.7)0.6
}@article{DBLP:journals/corr/KeuperTYABS16, author = {Margret Keuper and Siyu Tang and Zhongjie Yu and Bjoern Andres and Thomas Brox and Bernt Schiele}, title = {A Multi-cut Formulation for Joint Segmentation and Tracking of Multiple Objects}, journal = {CoRR}, volume = {abs/1607.06317}, year = {2016}, url = {http://arxiv.org/abs/1607.06317}, timestamp = {Wed, 07 Jun 2017 14:41:31 +0200}, biburl = {http://dblp.uni-trier.de/rec/bib/journals/corr/KeuperTYABS16}, bibsource = {dblp computer science bibliography, http://dblp.org} }
YOONKJ16
74. online method using public detections
47.0
±8.1
50.1 38.5 125 (16.5)317 (41.8)7,901 88,179 51.6 92.3 39.0 38.3 43.3 71.1 40.7 72.7 79.0 1.3 627 (12.1)945 (18.3)3.5
K. YOON, J. GWAK, Y. SONG, Y. YOON, M. JEON. OneShotDA: Online Multi-object Tracker with One-shot-learning-based Data Association. In IEEE Access, 2020.
CS_MOT
75. online method using public detections
46.7
±10.6
51.5 37.6 76 (10.0)332 (43.7)5,941 90,566 50.3 93.9 39.0 36.7 41.6 72.9 38.6 72.1 77.9 1.0 619 (12.3)2,981 (59.2)1.2
A Cost Matrix Optimization Method Based on Spatial Constraints under Hungarian Algorithm
NOMT
76. using public detections
46.4
±8.9
53.3 41.7 139 (18.3)314 (41.4)9,753 87,565 52.0 90.7 45.6 38.6 48.6 76.7 41.4 72.2 79.7 1.6 359 (6.9)504 (9.7)2.6
W. Choi. Near-Online Multi-target Tracking with Aggregated Local Flow Descriptor. In ICCV, 2015.
JMC
77. using public detections
46.3
±9.4
46.3 36.2 118 (15.5)301 (39.7)6,373 90,914 50.1 93.5 35.6 37.1 37.5 76.2 39.2 73.2 79.1 1.1 657 (13.1)1,114 (22.2)0.8
S. Tang, B. Andres, M. Andriluka, B. Schiele. Multi-Person Tracking by Multicuts and Deep Matching. In BMTT, 2016.
DD_TAMA16
78. online method using public detections
46.2
±8.4
49.4 37.3 107 (14.1)334 (44.0)5,126 92,367 49.3 94.6 38.3 36.6 40.8 74.1 38.4 73.7 78.9 0.9 598 (12.1)1,127 (22.8)6.5
Y. Yoon, D. Kim, Y. Song, K. Yoon, M. Jeon. Online Multiple Pedestrians Tracking using Deep Temporal Appearance Matching Association. In Information Sciences, 2020.
DASOT16
79. online method using public detections
46.1
±9.2
49.4 37.9 111 (14.6)316 (41.6)8,222 89,204 51.1 91.9 38.5 37.5 41.5 73.1 39.9 71.8 78.7 1.4 802 (15.7)2,057 (40.3)9.0
Q. Chu, W. Ouyang, B. Liu, F. Zhu, N. Yu. DASOT: A Unified Framework Integrating Data Association and Single Object Tracking for Online Multi-Object Tracking. In Proceedings of the AAAI Conference on Artificial Intelligence, 2020.
DMAN
80. online method using public detections
46.1
±9.1
54.8 40.3 132 (17.4)324 (42.7)7,909 89,874 50.7 92.1 44.3 36.9 47.6 71.9 39.1 71.1 77.5 1.3 532 (10.5)1,616 (31.9)0.3
J. Zhu, H. Yang, N. Liu, M. Kim, W. Zhang, M. Yang. Online Multi-Object Tracking with Dual Matching Attention Networks. In ECCV, 2018.
TrackerMOTAIDF1HOTAMTMLFPFNRcllPrcnAssADetAAssReAssPrDetReDetPrLocAFAFID Sw.FragHz
EDR16
81. online method using public detections
46.1
±8.3
46.2 36.0 106 (14.0)289 (38.1)4,418 92,849 49.1 95.3 35.4 37.1 39.4 67.8 39.0 75.7 80.4 0.7 1,061 (21.6)3,102 (63.2)19.7
Z. Fu, X. Lai, S. Naqvi. Enhanced Detection Reliability for Human Tracking Based Video Analytics. In International Conference on Information Fusion (FUSION), 2019.
STAM16
82. online method using public detections
46.0
±9.1
50.0 37.9 111 (14.6)331 (43.6)6,895 91,117 50.0 93.0 39.3 36.7 41.5 75.6 38.9 72.3 78.6 1.2 473 (9.5)1,422 (28.4)0.2
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.
deepS2
83. using public detections
46.0
±8.2
46.5 36.0 118 (15.5)323 (42.6)5,124 92,697 49.2 94.6 35.2 37.0 37.1 76.5 38.8 74.7 79.7 0.9 693 (14.1)759 (15.4)0.7
ID 32
RAR16pub
84. online method using public detections
45.9
±9.7
48.8 36.5 100 (13.2)318 (41.9)6,871 91,173 50.0 93.0 36.3 36.8 38.1 75.8 38.9 72.4 78.5 1.2 648 (13.0)1,992 (39.8)0.9
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.
MHT_DAM
85. using public detections
45.8
±8.6
46.1 36.3 123 (16.2)328 (43.2)6,412 91,758 49.7 93.4 35.7 37.1 37.2 78.9 39.2 73.7 79.7 1.1 590 (11.9)781 (15.7)0.8
C. Kim, F. Li, A. Ciptadi, J. Rehg. Multiple Hypothesis Tracking Revisited. In ICCV, 2015.
MTDF
86. online method using public detections
45.7
±10.8
40.1 32.1 107 (14.1)276 (36.4)12,018 84,970 53.4 89.0 27.2 38.4 28.5 74.2 41.2 68.8 76.6 2.0 1,987 (37.2)3,377 (63.2)1.5
Z. Fu, F. Angelini, J. Chambers, S. Naqvi. Multi-Level Cooperative Fusion of GM-PHD Filters for Online Multiple Human Tracking. In IEEE Transactions on Multimedia, 2019.
INTERA_MOT
87. using public detections
45.4
±8.1
47.7 37.2 137 (18.1)294 (38.7)13,407 85,547 53.1 87.8 36.7 38.1 39.4 73.6 41.4 68.6 77.8 2.3 600 (11.3)930 (17.5)4.3
L. Lan, X. Wang, S. Zhang, D. Tao, W. Gao, T. Huang. Interacting Tracklets for Multi-object Tracking. In IEEE Transactions on Image Processing, 2018.
EDMT
88. using public detections
45.3
±9.1
47.9 37.3 129 (17.0)303 (39.9)11,122 87,890 51.8 89.5 37.0 38.0 38.8 77.5 41.0 70.9 79.2 1.9 639 (12.3)946 (18.3)1.8
J. Chen, H. Sheng, Y. Zhang, Z. Xiong. Enhancing Detection Model for Multiple Hypothesis Tracking. In BMTT-PETS CVPRw, 2017.
DCCRF16
89. online method using public detections
44.8
±9.5
39.7 32.0 107 (14.1)321 (42.3)5,613 94,133 48.4 94.0 28.7 36.0 29.8 77.9 37.8 73.5 79.1 0.9 968 (20.0)1,378 (28.5)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.
TBSS
90. online method using public detections
44.6
±9.3
42.6 33.0 93 (12.3)333 (43.9)4,136 96,128 47.3 95.4 31.1 35.1 32.7 74.2 36.7 74.0 78.8 0.7 790 (16.7)1,419 (30.0)3.0
X. Zhou, P. Jiang, Z. Wei, H. Dong, F. Wang. Online Multi-Object Tracking with Structural Invariance Constraint. In BMVC, 2018.
TrackerMOTAIDF1HOTAMTMLFPFNRcllPrcnAssADetAAssReAssPrDetReDetPrLocAFAFID Sw.FragHz
OTCD_1
91. online method using public detections
44.4
±10.8
45.6 35.2 88 (11.6)361 (47.6)5,759 94,927 47.9 93.8 34.8 35.7 37.3 72.2 37.5 73.4 78.7 1.0 759 (15.8)1,787 (37.3)17.6
Q. Liu, B. Liu, Y. Wu, W. Li, N. Yu. Real-Time Online Multi-Object Tracking in Compressed Domain. In IEEE Access, 2019.
QuadMOT16
92. using public detections
44.1
±9.4
38.3 30.9 111 (14.6)341 (44.9)6,388 94,775 48.0 93.2 26.7 35.8 27.9 77.4 37.9 73.5 79.8 1.1 745 (15.5)1,096 (22.8)1.8
J. Son, M. Baek, M. Cho, B. Han. Multi-Object Tracking with Quadruplet Convolutional Neural Networks. In CVPR, 2017.
CDA_DDALv2
93. online method using public detections
43.9
±7.8
45.1 34.0 81 (10.7)337 (44.4)6,450 95,175 47.8 93.1 33.1 35.1 35.8 69.0 37.1 72.2 78.2 1.1 676 (14.1)1,795 (37.6)0.5
S. Bae and K. Yoon, Confidence-Based Data Association and Discriminative Deep Appearance Learning for Robust Online Multi-Object Tracking , In IEEE TPAMI, 2017.
LFNF16
94. using public detections
43.6
±8.4
41.6 33.5 101 (13.3)347 (45.7)6,616 95,363 47.7 92.9 31.7 35.7 33.8 74.8 37.8 73.6 79.8 1.1 836 (17.5)938 (19.7)0.6
Sheng H, Hao L, Chen J, et al. Robust Local Effective Matching Model for Multi-Target Tracking. In PCM, 2017
MOT_GM_
95. online method using public detections
43.2
±12.0
51.5 37.9 68 (9.0)414 (54.5)3,481 99,532 45.4 96.0 42.5 33.9 46.2 70.4 35.3 74.6 79.0 0.6 484 (0.0)1,461 (0.0)7.9
Y. Yoo, S. Lee, S. Bae. Effective Multi-Object Tracking via Global Object Models and Object Constraint Learning. In , .
oICF
96. online method using public detections
43.2
±10.6
49.3 36.2 86 (11.3)368 (48.5)6,651 96,515 47.1 92.8 38.5 34.2 44.0 65.7 36.2 71.4 78.0 1.1 381 (8.1)1,404 (29.8)0.4
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.
MHT_bLSTM6
97. using public detections
42.1
±8.6
47.8 36.7 113 (14.9)337 (44.4)11,637 93,172 48.9 88.5 37.9 35.7 42.3 72.2 38.6 69.8 79.0 2.0 753 (15.4)1,156 (23.6)1.8
C. Kim, F. Li, J. Rehg. Multi-object Tracking with Neural Gating Using Bilinear LSTM. In ECCV, 2018.
TestUnsup
98. online method using public detections
41.5
±9.0
44.9 0.0 104 (13.7)330 (43.5)12,596 93,404 48.8 87.6 0.0 0.0 0.0 0.0 0.0 0.0 100.0 2.1 643 (13.2)796 (16.3)19.7
Multi Object Tracking using Deep Structural Cost Minimization in Data Association
LINF1
99. using public detections
41.0
±10.1
45.7 34.7 88 (11.6)389 (51.3)7,896 99,224 45.6 91.3 36.3 33.3 38.1 75.7 35.3 70.8 78.5 1.3 430 (9.4)963 (21.1)4.2
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.
PHD_GSDL16
100. online method using public detections
41.0
±8.9
43.1 33.1 86 (11.3)315 (41.5)6,498 99,257 45.6 92.7 32.6 33.8 35.6 69.4 35.7 72.7 79.1 1.1 1,810 (39.7)3,650 (80.1)8.3
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.
TrackerMOTAIDF1HOTAMTMLFPFNRcllPrcnAssADetAAssReAssPrDetReDetPrLocAFAFID Sw.FragHz
GMPHD_ReId
101. online method using public detections
40.4
±9.3
50.1 36.0 87 (11.5)327 (43.1)6,569 101,251 44.5 92.5 39.5 32.9 43.1 73.7 34.7 72.2 78.7 1.1 789 (17.7)2,519 (56.6)31.6
N. Baisa. Occlusion-robust online multi-object visual tracking using a GM-PHD filter with CNN-based re-identification. In Journal of Visual Communication and Image Representation, 2021.
PMPTracker
102. online method using public detections
40.3
±11.7
38.2 30.6 79 (10.4)319 (42.0)10,071 97,524 46.5 89.4 28.4 33.6 30.5 69.3 35.9 69.0 76.9 1.7 1,343 (28.9)2,764 (59.4)148.0
Light version of PTZ camera Mutiple People Tracker
AM_ADM
103. online method using public detections
40.1
±10.1
43.8 33.4 54 (7.1)351 (46.2)8,503 99,891 45.2 90.6 33.8 33.3 35.6 74.6 35.5 71.1 78.8 1.4 789 (17.5)1,736 (38.4)5.8
S. Lee, M. Kim, S. Bae, Learning Discriminative Appearance Models for Online Multi-Object Tracking with Appearance Discriminability Measures, In IEEE Access, 2018.
SDMT
104. online method using public detections
39.6
±8.1
42.3 32.8 89 (11.7)373 (49.1)11,130 98,343 46.1 88.3 32.1 33.6 35.0 70.1 36.2 69.4 78.6 1.9 602 (13.1)772 (16.8)19.8
M. Thoreau, N. Kottege. Deep Similarity Metric Learning for Real-Time Pedestrian Tracking. In arXiv, 2018.
CDF17
105. online method using public detections
39.3
±10.4
33.6 29.0 95 (12.5)310 (40.8)12,430 93,394 48.8 87.7 24.0 35.5 24.9 77.9 38.4 69.1 78.1 2.1 4,934 (101.2)5,886 (120.7)9.7
Z. Fu, S. Naqvi, J. Chambers. Collaborative Detector Fusion of Data-Driven PHD Filter for Online Multiple Human Tracking. In 2018 21st International Conference on Information Fusion (FUSION), 2018.
EAMTT_pub
106. online method using public detections
38.8
±8.6
42.4 32.5 60 (7.9)373 (49.1)8,114 102,452 43.8 90.8 32.9 32.3 36.6 66.2 34.3 71.0 78.3 1.4 965 (22.0)1,657 (37.8)11.8
R. Sanchez-Matilla, F. Poiesi, A. Cavallaro "Multi-target tracking with strong and weak detections" in BMTT ECCVw 2016
OVBT
107. online method using public detections
38.4
±8.6
37.8 29.9 57 (7.5)359 (47.3)11,517 99,463 45.4 87.8 27.2 33.2 28.5 74.3 35.8 69.1 78.8 1.9 1,321 (29.1)2,140 (47.1)0.3
Y. Ban, S. Ba, X. Alameda-Pineda, R. Horaud. Tracking Multiple Persons Based on a Variational Bayesian Model. In BMTT 2016, .
GMMCP
108. using public detections
38.1
±7.8
35.5 28.7 65 (8.6)386 (50.9)6,607 105,315 42.2 92.1 26.3 31.4 27.4 76.3 33.2 72.3 79.1 1.1 937 (22.2)1,669 (39.5)0.5
A. Dehghan, S. Assari, M. Shah.. GMMCP-Tracker:Globally Optimal Generalized Maximum Multi Clique Problem for Multiple Object Tracking. In CVPR, 2015.
LTTSC-CRF
109. using public detections
37.6
±8.0
42.1 33.1 73 (9.6)419 (55.2)11,969 101,343 44.4 87.1 33.3 32.9 36.1 70.8 35.5 69.6 78.9 2.0 481 (10.8)1,012 (22.8)0.6
N. Le, A. Heili, M. Odobez. Long-Term Time-Sensitive Costs for CRF-Based Tracking by Detection. In ECCVw, 2016.
HISP_DAL
110. online method using public detections
37.4
±8.8
30.5 25.7 58 (7.6)386 (50.9)3,222 108,865 40.3 95.8 21.9 30.4 22.7 78.7 31.6 75.1 79.6 0.5 2,101 (52.1)2,151 (53.4)3.3
N. Baisa. Robust online multi-target visual tracking using a HISP filter with discriminative deep appearance learning. In Journal of Visual Communication and Image Representation, 2021.
TrackerMOTAIDF1HOTAMTMLFPFNRcllPrcnAssADetAAssReAssPrDetReDetPrLocAFAFID Sw.FragHz
JCmin_MOT
111. online method using public detections
36.7
±9.1
36.2 28.6 57 (7.5)413 (54.4)2,936 111,890 38.6 96.0 28.5 29.0 29.7 77.0 30.0 74.7 79.3 0.5 667 (17.3)831 (21.5)14.8
M. Abhijeet Boragule. Joint Cost Minimization for Multi-Object Tracking. In 2017 IEEE International Conference on Advanced Vide and Signale Based Surveillance, 2017.
HISP_T
112. online method using public detections
35.9
±8.7
28.9 24.9 59 (7.8)380 (50.1)6,412 107,918 40.8 92.1 20.5 30.5 21.2 77.0 32.1 72.4 79.3 1.1 2,594 (63.6)2,298 (56.3)4.8
N. Baisa. Online Multi-target Visual Tracking using a HISP Filter. In Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5: VISAPP,, 2018.
LP2D
113. using public detections
35.7
±12.3
34.2 27.4 66 (8.7)385 (50.7)5,084 111,163 39.0 93.3 26.0 29.0 27.4 73.2 30.4 72.7 79.1 0.9 915 (23.4)1,264 (32.4)49.3
MOT baseline: Linear programming on 2D image coordinates.
GM_PHD_DAL
114. online method using public detections
35.1
±9.1
26.6 23.0 53 (7.0)390 (51.4)2,350 111,886 38.6 96.8 18.3 29.3 19.5 69.7 30.3 75.8 79.9 0.4 4,047 (104.8)5,338 (138.2)3.5
N. Baisa. Online Multi-object Visual Tracking using a GM-PHD Filter with Deep Appearance Learning. In 2019 22th International Conference on Information Fusion (FUSION), 2019.
TBD
115. using public detections
33.7
±8.8
0.0 0.0 55 (7.2)411 (54.2)5,804 112,587 38.2 92.3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 2,418 (63.2)2,252 (58.9)1.3
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.
GM_PHD_N1T
116. online method using public detections
33.3
±9.0
25.5 22.6 42 (5.5)425 (56.0)1,750 116,452 36.1 97.4 18.7 27.7 19.5 77.6 28.5 76.9 80.1 0.3 3,499 (96.8)3,594 (99.5)9.9
N. Baisa, A. Wallace. Development of a N-type GM-PHD filter for multiple target, multiple type visual tracking. In Journal of Visual Communication and Image Representation, 2019.
CEM
117. using public detections
33.2
±8.8
0.0 0.0 59 (7.8)413 (54.4)6,837 114,322 37.3 90.9 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.2 642 (17.2)731 (19.6)5,919.0
A. Milan, S. Roth, K. Schindler. Continuous Energy Minimization for Multitarget Tracking. In IEEE TPAMI, 2014.
CppSORT
118. online method using public detections
31.5
±9.0
27.7 23.8 33 (4.3)455 (59.9)3,048 120,278 34.0 95.3 21.8 26.2 22.3 82.0 27.1 75.9 80.3 0.5 1,587 (46.6)2,239 (65.8)687.1
S. Murray. Real-Time Multiple Object Tracking - A Study on the Importance of Speed. In arXiv preprint arXiv:1709.03572, 2017.
LM_NN
119. using public detections
31.0
±7.2
31.5 0.0 56 (7.4)443 (58.4)2,451 122,649 32.7 96.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.4 678 (20.7)666 (20.3)3.0
ID NEUCOM-D-18-03230
GMPHD_HDA
120. online method using public detections
30.5
±6.9
33.4 25.9 35 (4.6)453 (59.7)5,169 120,970 33.6 92.2 26.9 25.1 29.6 70.3 26.2 71.9 78.7 0.9 539 (16.0)731 (21.7)13.6
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.
TrackerMOTAIDF1HOTAMTMLFPFNRcllPrcnAssADetAAssReAssPrDetReDetPrLocAFAFID Sw.FragHz
SMOT
121. using public detections
29.7
±8.8
0.0 0.0 40 (5.3)362 (47.7)17,426 107,552 41.0 81.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2.9 3,108 (75.8)4,483 (109.3)0.2
C. Dicle, O. Camps, M. Sznaier. The Way They Move: Tracking Targets with Similar Appearance. In ICCV, 2013.
JPDA_m
122. using public detections
26.2
±8.8
0.0 0.0 31 (4.1)512 (67.5)3,689 130,549 28.4 93.3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.6 365 (12.9)638 (22.5)22.2
H. Rezatofighi, A. Milan, Z. Zhang, Q. Shi, A. Dick, I. Reid. Joint Probabilistic Data Association Revisited. In ICCV, 2015.
DP_NMS
123. using public detections
26.2
±9.7
31.2 24.9 31 (4.1)512 (67.5)3,689 130,557 28.4 93.3 28.7 21.8 30.9 70.0 22.5 73.9 79.3 0.6 365 (12.9)638 (22.5)212.6
H. Pirsiavash, D. Ramanan, C. Fowlkes. Globally-Optimal Greedy Algorithms for Tracking a Variable Number of Objects. In CVPR, 2011.
Fa_m
124. using public detections
-15.9
±112.9
21.2 24.6 152 (20.0)398 (52.4)66,233 144,576 20.7 36.3 47.3 12.9 52.0 72.8 17.6 30.8 81.6 11.2 522 (25.2)1,355 (65.4)33.8
SequencesFramesTrajectoriesBoxes
75919759182326

Difficulty Analysis

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

MOT16-03

MOT16-03

(59.0 MOTA)

MOT16-06

MOT16-06

(47.9 MOTA)

MOT16-12

MOT16-12

(43.8 MOTA)

...

...

MOT16-08

MOT16-08

(34.4 MOTA)

MOT16-14

MOT16-14

(26.2 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.
HOTA higher 100%Higher Order Tracking Accuracy [3]. Geometric mean of detection accuracy and association accuracy. Averaged across localization thresholds.
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).
Rcll higher 100%Ratio of correct detections to total number of GT boxes.
Prcn higher 100%Ratio of TP / (TP+FP).
AssA higher 100%Association Accuracy [3]. Association Jaccard index averaged over all matching detections and then averaged over localization thresholds.
DetA higher 100%Detection Accuracy [3]. Detection Jaccard index averaged over localization thresholds.
AssRe higher 100%Association Recall [3]. TPA / (TPA + FNA) averaged over all matching detections and then averaged over localization thresholds.
AssPr higher 100%Association Precision [3]. TPA / (TPA + FPA) averaged over all matching detections and then averaged over localization thresholds.
DetRe higher 100%Detection Recall [3]. TP /(TP + FN) averaged over localization thresholds.
DetPr higher 100%Detection Precision [3]. TP /(TP + FP) averaged over localization thresholds.
LocA higher 100%Localization Accuracy [3]. Average localization similarity averaged over all matching detections and averaged over localization thresholds.
FAF lower 0The average number of false alarms per frame.
ID Sw. lower 0Number of Identity Switches (ID switch ratio = #ID switches / recall) [4]. 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] Jonathon Luiten, A.O. & Leibe, B. HOTA: A Higher Order Metric for Evaluating Multi-Object Tracking. International Journal of Computer Vision, 2020.
[4] 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.



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