MOT17 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

TrackerMOTAIDF1MT MLFPFNID Sw.FragHzDetector
Response17
1. using public detections
61.2
±14.3
63.236.7% 22.0% 55,168159,9863,589 (50.1)7,640 (106.6)5.9Public
Anonymous submission
TCT4
2. online method using public detections
50.7
±15.4
50.924.5% 25.6% 46,638224,9556,543 (108.8)7,968 (132.5)14.1Public
Anonymous submission
FFT
3. online method using public detections
56.5
±15.7
51.026.2% 26.7% 23,746215,9715,672 (91.9)5,474 (88.7)1.8Public
Anonymous submission
GNNT
4. online method using public detections
-523.7
±1,341.9
9.033.6% 27.2% 3,318,414185,81915,019 (223.9)4,763 (71.0)7.6Public
Anonymous submission
ISE_MOT17R
5. online method using public detections
60.1
±11.0
56.428.5% 28.1% 23,168199,4832,556 (39.5)3,182 (49.2)7.2Public
MIFT
TAR_1
6. online method using public detections
51.6
±11.9
41.421.7% 28.7% 33,514235,8593,629 (62.4)5,949 (102.2)5.6Public
Anonymous submission
TCT
7. online method using public detections
47.5
±27.1
49.323.2% 28.8% 52,209238,7165,541 (96.0)7,368 (127.7)14.1Public
Anonymous submission
ISE_MOT
8. online method using public detections
58.6
±10.5
54.727.0% 29.8% 23,033208,0452,368 (37.5)3,247 (51.4)16.3Public
Anonymous submission
CRF_TRA
9. using public detections
53.1
±12.2
53.724.2% 30.7% 27,194234,9912,518 (43.2)4,918 (84.3)1.4Public
Anonymous submission
baitrack
10. using public detections
37.6
±19.4
20.321.0% 30.9% 99,085244,0018,808 (155.2)6,708 (118.2)6.4Public
Anonymous submission
TrackerMOTAIDF1MT MLFPFNID Sw.FragHzDetector
cnt_klt
11. using public detections
48.0
±11.8
57.819.0% 31.6% 63,207228,7831,215 (20.4)4,134 (69.5)59.2Public
Anonymous submission
UTA
12. online method using public detections
53.1
±11.7
54.421.5% 31.8% 22,893239,5342,251 (39.1)6,192 (107.6)5.0Public
Anonymous submission
zxbtk17
13. online method using public detections
45.1
±14.7
40.017.7% 31.8% 33,186273,5313,303 (64.1)8,148 (158.1)8.3Public
Anonymous submission
ISDH_HDAv2
14. online method using public detections
54.5
±14.5
65.926.4% 32.1% 46,693207,0933,010 (47.6)6,000 (94.8)3.6Public
MM-008988/ IEEE Transactions on Multimedia
SCNet
15. online method using public detections
53.2
±15.4
54.920.0% 32.1% 30,440231,1092,621 (44.4)6,031 (102.2)1.0Public
Anonymous submission
AReid17
16. online method using public detections
51.4
±12.2
53.919.2% 32.3% 30,079241,3642,993 (52.3)6,373 (111.4)33.7Public
Anonymous submission
SMOT_no
17. online method using public detections
52.9
±12.3
54.118.7% 32.8% 26,703236,3462,702 (46.5)6,340 (109.1)4.9Public
Anonymous submission
MTDF17
18. online method using public detections
49.6
±13.9
45.218.9% 33.1% 37,124241,7685,567 (97.4)9,260 (162.0)1.2Public
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.
TOPA
19. online method using public detections
51.8
±13.5
53.419.6% 33.1% 27,603241,5462,668 (46.7)5,790 (101.2)443.9Public
Anonymous submission
GMPHD_Rd17
20. online method using public detections
46.8
±14.7
54.119.7% 33.3% 38,452257,6783,865 (71.1)8,097 (149.0)30.8Public
N. Baisa. Occlusion-robust Online Multi-object Visual Tracking using a GM-PHD Filter with a CNN-based Re-identification. In , 2019.
TrackerMOTAIDF1MT MLFPFNID Sw.FragHzDetector
SMOTe
21. online method using public detections
52.1
±12.1
53.818.4% 33.3% 27,571239,7242,691 (46.8)6,134 (106.7)1.0Public
Anonymous submission
FAMNet
22. online method using public detections
52.0
±12.0
48.719.1% 33.4% 14,138253,6163,072 (55.8)5,318 (96.6)0.0Public
P. Chu, H. Ling. FAMNet: Joint Learning of Feature, Affinity and Multi-dimensional Assignment for Online Multiple Object Tracking. In ICCV, 2019.
SNet_pub
23. online method using public detections
51.7
±12.0
53.418.0% 33.5% 26,809243,0662,735 (48.0)6,157 (108.2)4.9Public
Anonymous submission
Lif_T
24. using public detections
60.5
±15.2
65.627.0% 33.6% 14,966206,6191,189 (18.8)3,476 (54.8)0.5Public
Anonymous submission
DAIST_
25. online method using public detections
52.1
±12.5
53.819.7% 33.6% 29,931237,9132,689 (46.5)5,600 (96.8)6.9Public
Anonymous submission
SFS
26. online method using public detections
50.0
±12.2
51.819.2% 33.6% 44,810234,5502,993 (51.2)6,858 (117.4)0.9Public
Anonymous submission
MOT_HY
27. using public detections
47.3
±121.2
49.417.2% 33.8% 46,875246,0614,231 (75.0)8,188 (145.2)2.0Public
Anonymous submission
STRN_MOT17
28. online method using public detections
50.9
±11.6
56.018.9% 33.8% 25,295249,3652,397 (43.0)9,363 (167.8)13.8Public
J. Xu, Y. Cao, Z. Zhang, H. Hu. Spatial-Temporal Relation Networks for Multi-Object Tracking. In ICCV, 2019.
PV
29. online method using public detections
52.8
±14.1
51.819.7% 34.0% 15,884246,9393,711 (66.0)8,757 (155.7)3.5Public
Anonymous submission
ReID_Seq
30. online method using public detections
51.4
±12.7
49.220.3% 34.1% 23,045247,8853,226 (57.5)4,148 (74.0)14.0Public
Anonymous submission
TrackerMOTAIDF1MT MLFPFNID Sw.FragHzDetector
MPNTrack17
31. using public detections
55.7
±13.2
59.127.2% 34.4% 25,013223,5311,433 (23.7)3,122 (51.7)4.2Public
Anonymous submission
track_bnw
32. online method using public detections
56.7
±13.4
52.123.1% 34.5% 8,895233,2062,351 (40.1)3,155 (53.8)0.7Public
Anonymous submission
MOLF
33. online method using public detections
50.9
±12.3
46.718.3% 34.6% 29,398242,8374,535 (79.6)5,343 (93.8)30.5Public
Anonymous submission
GMOT
34. using public detections
55.4
±12.2
57.922.7% 34.7% 20,608229,5111,403 (23.7)2,765 (46.6)5.9Public
LXD, KHW @ HRI-SH
HDTR
35. using public detections
54.1
±11.4
48.423.3% 34.8% 18,002238,8181,895 (32.9)2,693 (46.7)1.8Public
M. Babaee, A. Athar, G. Rigoll. Multiple People Tracking Using Hierarchical Deep Tracklet Re-identification. In arXiv preprint arXiv:1811.04091, 2018.
ReTracktor
36. using public detections
55.1
±14.0
52.821.4% 34.9% 15,489235,6942,119 (36.4)4,725 (81.1)0.8Public
Anonymous submission
track_bin
37. online method using public detections
57.2
±13.3
54.822.6% 34.9% 9,462229,7922,353 (39.7)3,122 (52.7)0.7Public
Anonymous submission
Alex1
38. online method using public detections
56.1
±14.9
55.022.1% 34.9% 12,627232,9562,097 (35.7)3,398 (57.9)22.8Public
Anonymous submission
LSMT
39. online method using public detections
51.9
±12.0
53.517.4% 35.0% 18,672250,6622,257 (40.6)5,733 (103.2)8.9Public
Anonymous submission
TrajTrack
40. online method using public detections
56.0
±12.9
57.222.6% 35.1% 14,378231,2122,546 (43.1)3,452 (58.5)1.4Public
Anonymous submission
TrackerMOTAIDF1MT MLFPFNID Sw.FragHzDetector
FWT
41. using public detections
51.3
±13.1
47.621.4% 35.2% 24,101247,9212,648 (47.2)4,279 (76.3)0.2Public
R. Henschel, L. Leal-Taixé, D. Cremers, B. Rosenhahn. Fusion of Head and Full-Body Detectors for Multi-Object Tracking. In Trajnet CVPRW, 2018.
PHD_GM
42. online method using public detections
48.8
±13.4
43.219.1% 35.2% 26,260257,9714,407 (81.2)6,448 (118.8)22.3Public
R. Sanchez-Matilla, A. Cavallaro. A predictor of moving objects for First-Person vision. In Proceedings of IEEE International Conference Image Processing, 2019.
Tracktor++v2
43. online method using public detections
56.3
±13.3
55.121.1% 35.3% 8,866235,4491,987 (34.1)3,763 (64.6)1.5Public
P. Bergmann, T. Meinhardt, L. Leal-Taixé. Tracking without bells and whistles. In ICCV, 2019.
CoCT
44. online method using public detections
50.1
±12.2
55.623.9% 35.4% 49,887229,3912,075 (35.0)4,304 (72.5)57.8Public
Anonymous submission
DS_MOT
45. online method using public detections
56.1
±15.8
52.721.1% 35.4% 8,866235,4493,609 (61.9)3,777 (64.8)10.0Public
Anonymous submission
NOTA
46. using public detections
51.3
±11.7
54.517.1% 35.4% 20,148252,5312,285 (41.4)5,798 (105.0)17.8Public
L. Chen, H. Ai, R. Chen, Z. Zhuang. Aggregate Tracklet Appearance Features for Multi-Object Tracking. In IEEE Signal Processing Letters, 2019.
DGCT
47. using public detections
54.5
±13.1
51.321.0% 35.4% 10,471243,1432,865 (50.3)4,889 (85.9)7.0Public
CJY, HYW, KHW @ HRI-SH
AFN17
48. using public detections
51.5
±13.0
46.920.6% 35.5% 22,391248,4202,593 (46.3)4,308 (77.0)1.8Public
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.
DTBasline
49. online method using public detections
51.1
±11.7
53.416.7% 35.5% 20,309253,2452,549 (46.2)5,910 (107.2)22.2Public
Anonymous submission
eTC17
50. using public detections
51.9
±12.4
58.123.1% 35.5% 36,164232,7832,288 (38.9)3,071 (52.3)0.7Public
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.
TrackerMOTAIDF1MT MLFPFNID Sw.FragHzDetector
MOT17ZH
51. online method using public detections
51.1
±13.7
53.416.7% 35.5% 20,309253,2452,549 (46.2)5,910 (107.2)3.7Public
Anonymous submission
PHD_GSDL17
52. online method using public detections
48.0
±13.6
49.617.1% 35.6% 23,199265,9543,998 (75.6)8,886 (168.1)6.7Public
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.
tianyi
53. using public detections
50.0
±13.7
51.020.5% 35.6% 27,839251,1483,312 (59.7)6,234 (112.3)5.9Public
Anonymous submission
MOCL
54. online method using public detections new
49.5
±14.0
43.420.7% 35.6% 25,373254,1315,164 (94.0)5,787 (105.3)148.0Public
ECCV-20/4696
TTracker
55. online method using public detections
46.2
±14.0
44.021.0% 35.6% 44,854254,4384,258 (77.6)6,307 (114.9)29.6Public
Anonymous submission
MOTDT17
56. online method using public detections
50.9
±11.9
52.717.5% 35.7% 24,069250,7682,474 (44.5)5,317 (95.7)18.3Public
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.
FPSN
57. online method using public detections
44.9
±13.9
48.416.5% 35.8% 33,757269,9527,136 (136.8)14,491 (277.8)10.1Public
S. Lee, E. Kim. Multiple Object Tracking via Feature Pyramid Siamese Networks. In IEEE ACCESS, 2018.
JBNOT
58. using public detections
52.6
±12.3
50.819.7% 35.8% 31,572232,6593,050 (51.9)3,792 (64.5)5.4Public
R. Henschel, Y. Zou, B. Rosenhahn. Multiple People Tracking using Body and Joint Detections. In CVPRW, 2019.
SOTD_MC
59. online method using public detections
48.4
±15.0
45.519.4% 35.9% 33,525255,0912,531 (46.2)4,944 (90.2)67.0Public
Anonymous submission
TARCA
60. online method using public detections
55.9
±13.3
58.124.2% 35.9% 20,141227,1511,784 (29.9)3,741 (62.6)6.9Public
Anonymous submission
TrackerMOTAIDF1MT MLFPFNID Sw.FragHzDetector
LT17
61. online method using public detections
47.7
±16.6
45.217.3% 36.0% 27,856263,0624,042 (75.7)9,183 (172.0)7.2Public
Anonymous submission
overlap
62. using public detections
51.5
±13.1
55.623.0% 36.1% 38,322233,2751,860 (31.7)2,935 (50.0)66.9Public
Anonymous submission
Lab031
63. using public detections
46.9
±16.2
48.117.7% 36.1% 31,634263,9383,795 (71.3)10,498 (197.3)9.4Public
Anonymous submission
lbc_mot
64. using public detections
49.8
±14.3
52.320.3% 36.2% 20,963259,5382,638 (48.9)5,303 (98.2)66.9Public
Anonymous submission
MOTbyReID
65. online method using public detections
43.6
±13.7
37.117.6% 36.3% 35,725270,03612,347 (236.8)11,408 (218.8)2.5Public
Anonymous submission
EDMT17
66. using public detections
50.0
±13.9
51.321.6% 36.3% 32,279247,2972,264 (40.3)3,260 (58.0)0.6Public
J. Chen, H. Sheng, Y. Zhang, Z. Xiong. Enhancing Detection Model for Multiple Hypothesis Tracking. In BMTT-PETS CVPRw, 2017.
ResTestV2
67. using public detections
52.0
±16.4
50.919.2% 36.3% 33,320234,4812,836 (48.5)4,835 (82.7)66.9Public
Anonymous submission
MASS
68. online method using public detections
46.9
±14.1
46.016.9% 36.3% 25,733269,1164,478 (85.6)11,994 (229.3)17.1Public
H. Karunasekera, H. Wang, H. Zhang. Multiple Object Tracking With Attention to Appearance, Structure, Motion and Size. In IEEE Access, 2019.
HTBT
69. using public detections
52.3
±13.3
54.522.5% 36.4% 28,743238,2681,959 (33.9)2,973 (51.5)0.4Public
Anonymous submission
YoloSort
70. online method using public detections
29.5
±24.1
41.715.0% 36.4% 154,747238,2414,888 (84.6)4,952 (85.7)14.4Public
Anonymous submission
TrackerMOTAIDF1MT MLFPFNID Sw.FragHzDetector
TPbase17
71. online method using public detections
43.3
±15.0
48.216.2% 36.6% 49,992265,8154,194 (79.3)12,103 (228.8)22.2Public
Anonymous submission
Tracktor++
72. online method using public detections
53.5
±14.5
52.319.5% 36.6% 12,201248,0472,072 (37.0)4,611 (82.3)1.5Public
P. Bergmann, T. Meinhardt, L. Leal-Taixé. Tracking without bells and whistles. In ICCV, 2019.
LSST17O
73. online method using public detections
52.7
±13.3
57.917.9% 36.6% 22,512241,9362,167 (37.9)7,443 (130.3)1.8Public
Multi-Object Tracking with Multiple Cues and Switcher-Aware Classification
TriplDSort
74. using public detections
50.7
±15.3
50.520.3% 36.6% 51,739222,2124,397 (72.5)7,352 (121.3)0.6Public
Anonymous submission
MOT_TBC
75. using public detections
53.9
±15.7
50.020.2% 36.7% 24,584232,6702,945 (50.1)4,612 (78.5)6.7Public
Anonymous submission
OST
76. using public detections
49.7
±14.0
50.417.0% 36.7% 21,811258,6493,077 (56.8)4,339 (80.1)1.7Public
Anonymous submission
ENFT17
77. using public detections
52.8
±13.1
57.123.1% 36.8% 26,754237,9091,667 (28.8)2,557 (44.2)0.5Public
BUAA
MHT_DAM
78. using public detections
50.7
±13.7
47.220.8% 36.9% 22,875252,8892,314 (41.9)2,865 (51.9)0.9Public
C. Kim, F. Li, A. Ciptadi, J. Rehg. Multiple Hypothesis Tracking Revisited. In ICCV, 2015.
jCC
79. using public detections
51.2
±14.5
54.520.9% 37.0% 25,937247,8221,802 (32.1)2,984 (53.2)1.8Public
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.
SNM17
80. online method using public detections
46.8
±13.8
43.416.2% 37.1% 25,104271,0424,213 (81.1)9,891 (190.3)0.8Public
Anonymous submission
TrackerMOTAIDF1MT MLFPFNID Sw.FragHzDetector
TppNoFPN
81. using public detections
52.4
±15.3
52.618.5% 37.2% 18,635247,1042,726 (48.5)5,461 (97.2)4.2Public
Anonymous submission
JDT
82. online method using public detections
47.4
±12.2
50.116.8% 37.2% 26,910267,3312,760 (52.5)6,211 (118.0)35.1Public
Anonymous submission
Q_ls
83. online method using public detections
50.2
±14.4
43.619.7% 37.3% 23,143253,1514,414 (80.1)6,112 (110.9)1.8Public
Anonymous submission
YOONKJ17
84. online method using public detections
51.4
±13.5
54.021.2% 37.3% 29,051243,2022,118 (37.2)3,072 (54.0)3.4Public
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.
GNN_tracktor
85. online method using public detections
54.4
±12.9
54.117.8% 37.4% 12,655241,8682,660 (46.6)3,991 (69.9)1.7Public
Anonymous submission
E2EM
86. online method using public detections
47.5
±14.5
48.816.5% 37.5% 20,655272,1873,632 (70.2)12,712 (245.6)29.6Public
Anonymous submission
TPM
87. using public detections
54.2
±13.0
52.622.8% 37.5% 13,739242,7301,824 (32.0)2,472 (43.4)0.8Public
Anonymous submission
DEEP_TAMA
88. online method using public detections
50.3
±13.3
53.519.2% 37.5% 25,479252,9962,192 (39.7)3,978 (72.1)1.5Public
Y. Yoon, D. Kim, K. Yoon, Y. Song, M. Jeon. Online Multiple Pedestrian Tracking using Deep Temporal Appearance Matching Association. In arXiv:1907.00831, 2019.
MMHT17
89. online method using public detections
52.8
±12.9
53.320.3% 37.5% 25,401238,4132,596 (45.0)4,103 (71.1)37.2Public
Anonymous submission
MOT_AF
90. online method using public detections
53.5
±13.4
55.619.2% 37.6% 12,867247,8161,672 (29.8)3,516 (62.7)25.2Public
Anonymous submission
TrackerMOTAIDF1MT MLFPFNID Sw.FragHzDetector
PHD_LMP
91. online method using public detections
45.9
±13.1
42.515.5% 37.9% 27,946272,1964,977 (96.2)6,985 (135.0)29.4Public
Anonymous submission
eHAF17
92. using public detections
51.8
±13.2
54.723.4% 37.9% 33,212236,7721,834 (31.6)2,739 (47.2)0.7Public
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.
GMPHDOGM17
93. online method using public detections
49.9
±13.6
47.119.7% 38.0% 24,024255,2773,125 (57.1)3,540 (64.6)30.7Public
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.
OMHT
94. online method using public detections
52.6
±12.9
51.919.3% 38.0% 20,153244,9982,552 (45.1)4,148 (73.3)37.2Public
Anonymous submission
DAM_MOT
95. online method using public detections
47.0
±12.6
48.716.9% 38.1% 28,933267,8962,140 (40.7)2,756 (52.5)18.7Public
Multi Object Tracking using Deep Structural Cost Minimization in Data Association
MCLT17
96. using public detections
54.2
±12.3
63.524.0% 38.1% 23,602233,7831,208 (20.6)2,394 (40.9)66.9Public
Anonymous submission
TT17
97. using public detections
54.9
±12.8
63.124.4% 38.1% 20,236233,2951,088 (18.5)2,392 (40.8)2.5Public
TIP-21754-2019
98K
98. using public detections
40.8
±17.2
37.015.6% 38.1% 32,312298,1743,514 (74.5)4,991 (105.8)17.7Public
Anonymous submission
KVIOU
99. online method using public detections
46.6
±14.2
44.017.3% 38.1% 34,838262,0084,379 (81.8)7,844 (146.4)29.6Public
Anonymous submission
TLO17
100. online method using public detections
52.6
±12.9
51.319.5% 38.2% 20,089244,9302,530 (44.7)4,170 (73.7)25.2Public
Anonymous submission
TrackerMOTAIDF1MT MLFPFNID Sw.FragHzDetector
TTL
101. online method using public detections
52.3
±12.9
51.319.3% 38.2% 21,617244,5012,779 (49.0)4,290 (75.7)21.5Public
Anonymous submission
dcor
102. online method using public detections
45.0
±14.2
34.015.4% 38.2% 30,231275,2654,801 (93.7)8,498 (165.9)44.4Public
Anonymous submission
Seq2Seq
103. using public detections
52.7
±12.1
49.417.7% 38.2% 10,819253,8902,396 (43.6)3,374 (61.3)2.6Public
Anonymous submission
DMAN
104. online method using public detections
48.2
±12.3
55.719.3% 38.3% 26,218263,6082,194 (41.2)5,378 (100.9)0.3Public
J. Zhu, H. Yang, N. Liu, M. Kim, W. Zhang, M. Yang. Online Multi-Object Tracking with Dual Matching Attention Networks. In ECCV, 2018.
DeepMOTRPN
105. online method using public detections
48.1
±14.5
43.017.6% 38.6% 26,490262,5783,696 (69.1)5,353 (100.1)4.9Public
Anonymous submission
RFTracking
106. online method using public detections
48.5
±14.8
44.917.7% 38.6% 25,739261,7103,089 (57.6)4,813 (89.8)66.9Public
Anonymous submission
STCG17
107. using public detections
51.1
±12.9
54.520.4% 38.6% 32,258241,9161,702 (29.8)2,483 (43.5)66.9Public
Anonymous submission
cascademot
108. online method using public detections
41.8
±16.0
34.215.2% 38.7% 27,816288,76811,535 (236.3)14,800 (303.2)17.8Public
Anonymous submission
DeepMP17
109. using public detections
50.4
±13.1
52.318.8% 38.7% 22,535255,3561,868 (34.1)3,473 (63.4)7.4Public
TLO_MHT
110. online method using public detections
53.3
±12.9
53.320.0% 38.7% 22,161238,9592,434 (42.2)4,089 (70.9)2.0Public
Anonymous submission
TrackerMOTAIDF1MT MLFPFNID Sw.FragHzDetector
HISP_T17
111. online method using public detections
44.6
±14.2
38.815.1% 38.8% 25,478276,39510,617 (208.1)7,487 (146.8)4.7Public
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.
SiaIOU
112. using public detections
48.5
±16.7
48.518.9% 38.8% 26,867260,2783,152 (58.5)4,391 (81.5)8.3Public
Anonymous submission
wangs
113. online method using public detections
48.5
±14.2
45.018.3% 38.8% 25,428261,7553,233 (60.3)4,941 (92.2)66.9Public
Anonymous submission
Umot
114. online method using public detections
43.9
±13.8
37.815.2% 38.9% 28,596278,6219,363 (185.0)11,371 (224.6)19.7Public
Anonymous submission
GF
115. online method using public detections
45.0
±13.9
39.115.0% 39.0% 22,387277,33510,397 (204.5)7,421 (145.9)9.9Public
Anonymous submission
DualAtte
116. online method using public detections
48.4
±14.5
43.717.6% 39.0% 24,915262,6543,423 (64.0)5,192 (97.1)0.3Public
Anonymous submission
HISP_DAL17
117. online method using public detections
45.4
±13.9
39.914.8% 39.2% 21,820277,4738,727 (171.7)7,147 (140.6)3.2Public
N. Baisa. Robust Online Multi-target Visual Tracking using a HISP Filter with Discriminative Deep Appearance Learning. In CoRR, 2019.
DSA_MOT17
118. online method using public detections
45.0
±12.6
43.615.8% 39.2% 21,442286,4822,491 (50.6)3,824 (77.7)9.9Public
Anonymous submission
GMPHD_DAL
119. online method using public detections
44.4
±13.9
36.214.9% 39.4% 19,170283,38011,137 (223.7)13,900 (279.3)3.4Public
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.
GM_PHD_D
120. online method using public detections
44.0
±13.8
34.214.8% 39.4% 19,135283,53013,556 (272.5)13,821 (277.8)9.9Public
Anonymous submission
TrackerMOTAIDF1MT MLFPFNID Sw.FragHzDetector
MFT
121. online method using public detections
53.1
±16.1
50.120.4% 39.4% 35,295225,6063,681 (61.3)6,271 (104.5)0.7Public
Anonymous submission
AEb
122. using public detections
48.1
±13.6
46.017.7% 39.5% 16,839273,8192,350 (45.7)5,275 (102.5)66.9Public
2MPT
123. using public detections
48.1
±14.2
52.917.4% 39.6% 30,650260,1331,860 (34.5)2,784 (51.7)2.7Public
Anonymous submission
AM_ADM17
124. online method using public detections
48.1
±13.8
52.113.4% 39.7% 25,061265,4952,214 (41.8)5,027 (94.9)5.7Public
S. Lee, M. Kim, S. Bae, Learning Discriminative Appearance Models for Online Multi-Object Tracking with Appearance Discriminability Measures, In IEEE Access, 2018.
RegTL
125. using public detections
48.1
±13.7
41.618.1% 39.8% 20,850268,3633,386 (64.6)4,524 (86.3)17.8Public
Anonymous submission
QiMOT
126. online method using public detections
47.2
±13.1
40.815.5% 39.9% 18,907274,8284,320 (84.2)5,917 (115.4)1.8Public
Anonymous submission
ZM
127. online method using public detections
43.5
±13.9
32.614.5% 39.9% 25,083284,4059,197 (185.4)8,849 (178.4)14.4Public
Anonymous submission
DAN__test
128. using public detections
43.0
±14.7
43.313.5% 40.0% 30,367283,5337,576 (152.3)14,990 (301.3)1.8Public
Anonymous submission
ms_dh
129. online method using public detections
42.6
±14.6
40.113.6% 40.0% 31,878284,5287,446 (150.2)14,736 (297.3)4.0Public
Anonymous submission
MOTPP17
130. using public detections
52.4
±15.4
50.822.4% 40.0% 19,922246,1832,223 (39.4)2,769 (49.1)35.5Public
Anonymous submission
TrackerMOTAIDF1MT MLFPFNID Sw.FragHzDetector
PointMOT17
131. using public detections
52.2
±13.3
50.822.4% 40.0% 22,012245,2772,134 (37.8)2,652 (46.9)2.2Public
Anonymous submission
PPMOT
132. using public detections
52.4
±13.4
50.822.4% 40.0% 20,176246,1582,224 (39.5)2,769 (49.1)2.3Public
Anonymous submission
LSST17
133. using public detections
54.7
±12.9
62.320.4% 40.1% 26,091228,4341,243 (20.9)3,726 (62.6)1.5Public
Multi-Object Tracking with Multiple Cues and Switcher-Aware Classification
PPMOT17
134. using public detections
51.5
±13.0
47.821.8% 40.1% 19,821251,4952,492 (45.0)2,986 (53.9)35.5Public
Anonymous submission
PP17
135. using public detections
51.5
±13.0
47.821.8% 40.1% 19,821251,4952,492 (45.0)2,986 (53.9)1.9Public
Anonymous submission
IDGA
136. using public detections
52.6
±13.4
61.323.6% 40.2% 29,049236,8301,402 (24.2)2,613 (45.0)59.2Public
Anonymous submission
MHT_ReID7
137. using public detections
46.5
±13.7
46.918.8% 40.3% 22,203276,3743,386 (66.4)8,521 (167.0)1.6Public
Anonymous submission
MOTF17
138. using public detections
52.0
±13.2
50.520.1% 40.4% 19,222249,4642,293 (41.1)3,297 (59.1)2.2Public
Anonymous submission
DCORV2
139. online method using public detections
45.5
±13.9
36.114.6% 40.4% 21,161282,9013,592 (72.0)7,696 (154.4)35.5Public
Anonymous submission
IOU17
140. using public detections
45.5
±13.6
39.415.7% 40.5% 19,993281,6435,988 (119.6)7,404 (147.8)1,522.9Public
E. Bochinski, V. Eiselein, T. Sikora. High-Speed Tracking-by-Detection Without Using Image Information. In International Workshop on Traffic and Street Surveillance for Safety and Security at IEEE AVSS 2017, 2017.
TrackerMOTAIDF1MT MLFPFNID Sw.FragHzDetector
EDA_GNN
141. online method using public detections
45.5
±13.8
40.515.6% 40.6% 25,685277,6634,091 (80.5)5,579 (109.8)39.3Public
Paper ID 2713
OTCD_1
142. online method using public detections
48.6
±13.7
47.916.2% 41.2% 18,499268,2043,502 (66.7)5,588 (106.5)15.5Public
Q. Liu, B. Liu, Y. Wu, W. Li, N. Yu. Real-Time Online Multi-Object Tracking in Compressed Domain. In IEEE Access, 2019.
TM_track
143. online method using public detections
41.1
±14.9
32.813.2% 41.3% 27,606287,51117,408 (355.0)15,197 (309.9)2.5Public
Anonymous submission
Alex
144. online method using public detections
47.6
±14.2
49.813.2% 41.4% 16,028277,1102,731 (53.7)8,481 (166.7)0.2Public
Anonymous submission
AEb_O
145. online method using public detections
46.4
±13.9
44.916.5% 41.4% 17,030283,2652,266 (45.5)5,053 (101.5)1.8Public
Anonymous submission
HAM_SADF17
146. online method using public detections
48.3
±13.2
51.117.1% 41.7% 20,967269,0381,871 (35.8)3,020 (57.7)5.0Public
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.
MHT_bLSTM
147. using public detections
47.5
±12.6
51.918.2% 41.7% 25,981268,0422,069 (39.4)3,124 (59.5)1.9Public
C. Kim, F. Li, J. Rehg. Multi-object Tracking with Neural Gating Using Bilinear LSTM. In ECCV, 2018.
SRPN
148. online method using public detections
47.8
±13.2
41.417.0% 41.7% 38,279251,9894,325 (78.2)5,355 (96.8)11.8Public
Anonymous submission
XYHv2
149. online method using public detections
39.9
±12.4
23.89.9% 41.8% 29,713296,70412,900 (272.1)12,911 (272.3)7.8Public
Anonymous submission
JOINT_TRAC
150. using public detections
29.4
±17.6
34.512.6% 42.3% 132,192260,8085,397 (100.4)10,704 (199.0)66.9Public
Anonymous submission
TrackerMOTAIDF1MT MLFPFNID Sw.FragHzDetector
SORT17
151. online method using public detections
43.1
±13.3
39.812.5% 42.3% 28,398287,5824,852 (99.0)7,127 (145.4)143.3Public
A. Bewley, Z. Ge, L. Ott, F. Ramos, B. Upcroft. Simple online and realtime tracking. In 2016 IEEE International Conference on Image Processing (ICIP), 2016.
CTRACKER
152. online method using public detections
39.4
±13.5
26.113.4% 42.5% 16,249307,90017,592 (387.2)14,508 (319.3)66.9Public
Anonymous submission
GMPHD_N1Tr
153. online method using public detections
42.1
±13.2
33.911.9% 42.7% 18,214297,64610,698 (226.4)10,864 (229.9)9.9Public
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.
GM_PHD
154. online method using public detections
42.1
±13.2
33.911.9% 42.7% 18,214297,64610,698 (226.4)10,864 (229.9)9.9Public
Anonymous submission
EAMTT
155. online method using public detections
42.6
±13.3
41.812.7% 42.7% 30,711288,4744,488 (91.8)5,720 (117.0)12.0Public
R. Sanchez-Matilla, F. Poiesi, A. Cavallaro. Online Multi-target Tracking with Strong and Weak Detections. In Computer Vision -- ECCV 2016 Workshops, 2016.
CMT
156. using public detections
51.8
±12.9
60.719.6% 42.8% 29,528240,9601,217 (21.2)2,008 (35.0)6.5Public
#Submission: TIP-21190-2019
GMPHD_SHA
157. online method using public detections
43.7
±12.5
39.211.7% 43.0% 25,935287,7583,838 (78.3)5,056 (103.2)9.2Public
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.
UNS20
158. online method using public detections
46.5
±13.6
47.716.3% 43.1% 19,283280,7881,967 (39.2)3,103 (61.8)12.2Public
Anonymous submission
GOTURN_3B
159. online method using public detections
44.3
±13.7
38.513.0% 43.2% 30,302279,1444,861 (96.2)5,277 (104.4)48.6Public
Anonymous submission
GMPHD_KCF
160. online method using public detections
39.6
±13.5
36.68.8% 43.3% 50,903284,2285,811 (117.1)7,414 (149.4)3.3Public
T. Kutschbach, E. Bochinski, V. Eiselein, T. Sikora. Sequential Sensor Fusion Combining Probability Hypothesis Density and Kernelized Correlation Filters for Multi-Object Tracking in Video Data. In International Workshop on Traffic and Street Surveillance for Safety and Security at IEEE AVSS 2017, 2017.
TrackerMOTAIDF1MT MLFPFNID Sw.FragHzDetector
TLMHT
161. using public detections
50.6
±12.5
56.517.6% 43.4% 22,213255,0301,407 (25.7)2,079 (37.9)2.6Public
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.
CASC_MOT
162. online method using public detections
42.3
±12.8
46.89.1% 44.1% 21,035300,7973,616 (77.4)16,656 (356.7)11.4Public
Anonymous submission
SAS_MOT17
163. using public detections
44.2
±12.2
57.216.1% 44.3% 29,473283,6111,529 (30.7)2,644 (53.2)4.8Public
A. Maksai, P. Fua. Eliminating Exposure Bias and Metric Mismatch in Multiple Object Tracking. In CVPR, 2019.
D_SST_V1
164. online method using public detections
42.7
±13.9
46.111.8% 44.4% 18,861298,9895,531 (117.7)13,775 (293.0)2.3Public
Anonymous submission
CGHA_MOT
165. online method using public detections
41.2
±14.1
44.08.3% 45.9% 25,462299,1127,294 (155.2)18,655 (397.0)11.4Public
Anonymous submission
LM_NN
166. using public detections
45.1
±13.3
43.214.8% 46.2% 10,834296,4512,286 (48.2)2,463 (51.9)0.9Public
M. Babaee, Z. Li, G. Rigoll. A Dual CNN--RNN for Multiple People Tracking. In Neurocomputing, 2019.
GoturnM17
167. online method using public detections
38.3
±9.0
25.79.4% 47.1% 55,381282,67010,328 (207.0)9,849 (197.4)11.8Public
Anonymous submission
SRPN17
168. online method using public detections
40.8
±15.2
40.512.0% 48.0% 9,293321,8112,801 (65.2)7,120 (165.7)4.1Public
Anonymous submission
c3d_Track
169. online method using public detections
41.5
±13.7
40.210.7% 48.5% 33,332292,9313,890 (80.9)11,454 (238.2)22.2Public
Anonymous submission
GNNMOT
170. online method using public detections
42.0
±12.8
29.312.0% 50.0% 23,294299,6944,377 (93.4)3,847 (82.1)177.6Public
Anonymous submission
TrackerMOTAIDF1MT MLFPFNID Sw.FragHzDetector
GLMBS3
171. using public detections
38.0
±13.7
32.39.3% 52.8% 38,874304,0166,963 (151.0)3,927 (85.2)4.9Public
Anonymous submission
GM_PHD
172. online method using public detections
36.4
±14.1
33.94.1% 57.3% 23,723330,7674,607 (111.3)11,317 (273.5)38.4Public
V. Eiselein, D. Arp, M. Pätzold, T. Sikora. Real-time Multi-Human Tracking using a Probability Hypothesis Density Filter and multiple detectors. In 9th IEEE International Conference on Advanced Video and Signal-Based Surveillance, 2012.
DS_TW_F
173. online method using public detections
45.7
±27.0
50.910.8% 75.4% 6,528298,3681,329 (28.2)3,180 (67.5)66.9Public
Anonymous submission
OLGT_new
174. online method using public detections
45.7
±22.8
49.410.8% 75.5% 6,915298,2881,418 (30.1)3,641 (77.2)6.1Public
Anonymous submission
MOT_BJ
175. online method using public detections
-7.3
±23.5
1.40.0% 99.1% 52,007548,5314,824 (1,734.0)8,621 (3,098.8)0.0Public
Anonymous submission
SequencesFramesTrajectoriesBoxes
21177572355564228

Difficulty Analysis

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

MOT17-03-SDP

MOT17-03-SDP

(66.8% MOTA)

MOT17-03-FRCNN

MOT17-03-FRCNN

(59.7% MOTA)

MOT17-03-DPM

MOT17-03-DPM

(51.7% MOTA)

...

...

MOT17-14-DPM

MOT17-14-DPM

(21.3% MOTA)

MOT17-14-FRCNN

MOT17-14-FRCNN

(20.6% MOTA)


Evaluation Measures

Lower is better. Higher is better.
Measure Better Perfect Description
MOTA higher 100 % Multiple Object Tracking Accuracy [1]. This measure combines three error sources: false positives, missed targets and identity switches.
MOTP higher 100 % Multiple Object Tracking Precision [1]. The misalignment between the annotated and the predicted bounding boxes.
IDF1 higher 100 % ID F1 Score [2]. The ratio of correctly identified detections over the average number of ground-truth and computed detections.
FAF lower 0 The average number of false alarms per frame.
MT higher 100 % Mostly tracked targets. The ratio of ground-truth trajectories that are covered by a track hypothesis for at least 80% of their respective life span.
ML lower 0 % Mostly lost targets. The ratio of ground-truth trajectories that are covered by a track hypothesis for at most 20% of their respective life span.
FP lower 0 The total number of false positives.
FN lower 0 The total number of false negatives (missed targets).
ID Sw. lower 0 The total number of identity switches. Please note that we follow the stricter definition of identity switches as described in [3].
Frag lower 0 The total number of times a trajectory is fragmented (i.e. interrupted during tracking).
Hz higher Inf. Processing speed (in frames per second excluding the detector) on the benchmark. The frequency is provided by the authors and not officially evaluated by the MOTChallenge.

Legend

Symbol Description
online method This is an online (causal) method, i.e. the solution is immediately available with each incoming frame and cannot be changed at any later time.
using public detections This method used the provided detection set as input.
new This entry has been submitted or updated less than a week ago.

References:


[1] Bernardin, K. & Stiefelhagen, R. Evaluating Multiple Object Tracking Performance: The CLEAR MOT Metrics. Image and Video Processing, 2008(1):1-10, 2008.
[2] Ristani, E., Solera, F., Zou, R., Cucchiara, R. & Tomasi, C. Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking. In ECCV workshop on Benchmarking Multi-Target Tracking, 2016.
[3] Li, Y., Huang, C. & Nevatia, R. Learning to associate: HybridBoosted multi-target tracker for crowded scene. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2009.