MOT17 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!

TrackerAvg RankMOTA IDF1MTMLFPFNID Sw.FragHzDetector
MOT_BJ
1. online method using public detections 		93.2
-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
baitrack
2. using public detections 		56.3
37.6
±19.4
20.321.0% 30.9% 99,085244,0018,808 (155.2)6,708 (118.2)6.4Public
Anonymous submission
XYHv2
3. online method using public detections 		88.0
39.9
±12.4
23.89.9% 41.8% 29,713296,70412,900 (272.1)12,911 (272.3)7.8Public
Anonymous submission
GLMBS3
4. using public detections 		79.3
38.0
±13.7
32.39.3% 52.8% 38,874304,0166,963 (151.0)3,927 (85.2)4.9Public
Anonymous submission
ZM
5. online method using public detections 		77.0
43.5
±13.9
32.614.5% 39.9% 25,083284,4059,197 (185.4)8,849 (178.4)14.4Public
Anonymous submission
TM_track
6. online method using public detections 		87.5
41.1
±14.9
32.813.2% 41.3% 27,606287,51117,408 (355.0)15,197 (309.9)2.5Public
Anonymous submission
GM_PHD
7. online method using public detections 		72.6
42.1
±13.0
33.911.9% 42.7% 18,214297,64610,698 (226.4)10,864 (229.9)9.9Public
Anonymous submission
GMPHD_N1Tr
8. online method using public detections 		73.3
42.1
±13.5
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
9. online method using public detections 		75.4
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.
dcor
10. online method using public detections 		62.6
45.0
±14.2
34.015.4% 38.2% 30,231275,2654,801 (93.7)8,498 (165.9)44.4Public
Anonymous submission
TrackerAvg RankMOTA IDF1MTMLFPFNID Sw.FragHzDetector
cascademot
11. online method using public detections new 		72.8
41.6
±15.9
34.214.6% 41.8% 25,930292,50611,231 (233.2)14,385 (298.7)17.8Public
Anonymous submission
GM_PHD_D
12. online method using public detections 		68.6
44.0
±13.8
34.214.8% 39.4% 19,135283,53013,556 (272.5)13,821 (277.8)9.9Public
Anonymous submission
DCORV2
13. online method using public detections 		59.5
45.5
±13.9
36.114.6% 40.4% 21,161282,9013,592 (72.0)7,696 (154.4)35.5Public
Anonymous submission
GMPHD_DAL
14. online method using public detections 		70.2
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 22nd International Conference on Information Fusion, 2019.
GMPHD_KCF
15. online method using public detections 		81.9
39.6
±13.6
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.
98K
16. using public detections 		62.0
40.8
±17.2
37.015.6% 38.1% 32,312298,1743,514 (74.5)4,991 (105.8)17.7Public
Anonymous submission
MOTbyReID
17. online method using public detections 		73.1
43.6
±13.7
37.117.6% 36.3% 35,725270,03612,347 (236.8)11,408 (218.8)2.5Public
Anonymous submission
Umot
18. online method using public detections new 		75.1
43.9
±13.8
37.815.2% 38.9% 28,596278,6219,363 (185.0)11,371 (224.6)19.7Public
Anonymous submission
GOTURN_3B
19. online method using public detections 		67.7
44.3
±13.7
38.513.0% 43.2% 30,302279,1444,861 (96.2)5,277 (104.4)48.6Public
Anonymous submission
HISP_T17
20. online method using public detections 		69.3
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.
TrackerAvg RankMOTA IDF1MTMLFPFNID Sw.FragHzDetector
GF
21. online method using public detections 		64.2
45.0
±13.9
39.115.0% 39.0% 22,387277,33510,397 (204.5)7,421 (145.9)9.9Public
Anonymous submission
GMPHD_SHA
22. online method using public detections 		68.3
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.
IOU17
23. using public detections 		60.5
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.
SORT17
24. online method using public detections 		73.8
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.
HISP_DAL17
25. online method using public detections 		67.3
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.
zxbtk17
26. online method using public detections 		63.8
45.1
±14.7
40.017.7% 31.8% 33,186273,5313,303 (64.1)8,148 (158.1)8.3Public
Anonymous submission
ms_dh
27. online method using public detections 		80.4
42.6
±14.6
40.113.6% 40.0% 31,878284,5287,446 (150.2)14,736 (297.3)4.0Public
Anonymous submission
c3d_Track
28. online method using public detections 		73.3
41.5
±13.7
40.210.7% 48.5% 33,332292,9313,890 (80.9)11,454 (238.2)22.2Public
Anonymous submission
EDA_GNN
29. online method using public detections 		58.6
45.5
±13.8
40.515.6% 40.6% 25,685277,6634,091 (80.5)5,579 (109.8)39.3Public
Paper ID 2713
QiMOT
30. online method using public detections 		65.3
47.2
±13.1
40.815.5% 39.9% 18,907274,8284,320 (84.2)5,917 (115.4)1.8Public
Anonymous submission
TrackerAvg RankMOTA IDF1MTMLFPFNID Sw.FragHzDetector
TAR_1
31. online method using public detections 		47.9
51.6
±11.9
41.421.7% 28.7% 33,514235,8593,629 (62.4)5,949 (102.2)5.6Public
Anonymous submission
YoloSort
32. online method using public detections 		65.5
29.5
±24.1
41.715.0% 36.4% 154,747238,2414,888 (84.6)4,952 (85.7)14.4Public
Anonymous submission
EAMTT
33. online method using public detections 		69.7
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.
OTCD_1
34. online method using public detections 		61.9
44.9
±13.6
42.314.0% 44.2% 16,280291,1363,573 (73.8)5,444 (112.5)46.5Public
Q. Liu, B. Liu, Y. Wu, W. Li, N. Yu. Real-Time Online Multi-Object Tracking in Compressed Domain. In IEEE Access, 2019.
PHD_LMP
35. online method using public detections 		64.0
45.9
±13.1
42.515.5% 37.9% 27,946272,1964,977 (96.2)6,985 (135.0)29.4Public
Anonymous submission
DeepMOTRPN
36. online method using public detections 		53.1
48.1
±14.5
43.017.6% 38.6% 26,490262,5783,696 (69.1)5,353 (100.1)4.9Public
Anonymous submission
PHD_GM
37. online method using public detections 		51.9
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.
DAN__test
38. using public detections 		75.9
43.0
±14.7
43.313.5% 40.0% 30,367283,5337,576 (152.3)14,990 (301.3)1.8Public
Anonymous submission
SNM17
39. online method using public detections 		69.3
46.8
±13.8
43.416.2% 37.1% 25,104271,0424,213 (81.1)9,891 (190.3)0.8Public
Anonymous submission
Q_ls
40. online method using public detections 		56.2
50.2
±14.4
43.619.7% 37.3% 23,143253,1514,414 (80.1)6,112 (110.9)1.8Public
Anonymous submission
TrackerAvg RankMOTA IDF1MTMLFPFNID Sw.FragHzDetector
DSA_MOT17
41. online method using public detections 		51.3
45.0
±12.6
43.615.8% 39.2% 21,442286,4822,491 (50.6)3,824 (77.7)9.9Public
Anonymous submission
DualAtte
42. online method using public detections 		55.8
48.4
±14.5
43.717.6% 39.0% 24,915262,6543,423 (64.0)5,192 (97.1)0.3Public
Anonymous submission
CGHA_MOT
43. online method using public detections 		73.8
41.2
±14.1
44.08.3% 45.9% 25,462299,1127,294 (155.2)18,655 (397.0)11.4Public
Anonymous submission
AEb_O
44. online method using public detections 		53.0
46.4
±13.9
44.916.5% 41.4% 17,030283,2652,266 (45.5)5,053 (101.5)1.8Public
Anonymous submission
LT17
45. online method using public detections 		58.0
47.7
±16.6
45.217.3% 36.0% 27,856263,0624,042 (75.7)9,183 (172.0)7.2Public
Anonymous submission
MTDF17
46. online method using public detections 		59.6
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.
SOTD_MC
47. online method using public detections 		48.2
48.4
±15.0
45.519.4% 35.9% 33,525255,0912,531 (46.2)4,944 (90.2)67.0Public
Anonymous submission
MASS
48. online method using public detections 		58.6
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.
D_SST_V1
49. online method using public detections 		72.3
42.7
±13.9
46.111.8% 44.4% 18,861298,9895,531 (117.7)13,775 (293.0)2.3Public
Anonymous submission
CASC_MOT
50. online method using public detections 		67.5
42.3
±12.8
46.89.1% 44.1% 21,035300,7973,616 (77.4)16,656 (356.7)11.4Public
Anonymous submission
TrackerAvg RankMOTA IDF1MTMLFPFNID Sw.FragHzDetector
MHT_ReID7
51. using public detections 		62.8
46.5
±13.7
46.918.8% 40.3% 22,203276,3743,386 (66.4)8,521 (167.0)1.6Public
Anonymous submission
AFN17
52. using public detections 		36.3
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.
AEb
53. using public detections 		38.4
47.9
±13.6
47.018.1% 40.7% 15,828276,1792,082 (40.8)4,733 (92.7)66.9Public
Anonymous submission
GMPHDOGM17
54. online method using public detections 		40.4
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 Framework with the GMPHD Filter and Occlusion Group Management. In arXiv:1907.13347, 2019.
MHT_DAM
55. using public detections 		43.4
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.
FWT
56. using public detections 		40.9
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.
PPMOT17
57. using public detections 		37.6
51.5
±13.0
47.821.8% 40.1% 19,821251,4952,492 (45.0)2,986 (53.9)35.5Public
Anonymous submission
Lab031
58. using public detections 		57.7
46.9
±16.2
48.117.7% 36.1% 31,634263,9383,795 (71.3)10,498 (197.3)9.4Public
Anonymous submission
TPbase17
59. online method using public detections 		67.5
43.3
±15.0
48.216.2% 36.6% 49,992265,8154,194 (79.3)12,103 (228.8)22.2Public
Anonymous submission
FPSN
60. online method using public detections 		62.5
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.
TrackerAvg RankMOTA IDF1MTMLFPFNID Sw.FragHzDetector
HDTR
61. using public detections 		25.2
54.1
±11.4
48.423.3% 34.8% 18,002238,8181,895 (32.9)2,693 (46.7)1.8Public
SiaIOU
62. using public detections 		52.6
48.5
±16.7
48.518.9% 38.8% 26,867260,2783,152 (58.5)4,391 (81.5)8.3Public
Anonymous submission
DAM_MOT
63. online method using public detections 		42.7
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
FAMNet
64. online method using public detections 		40.1
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.
ReID_Seq
65. online method using public detections 		38.9
51.4
±12.7
49.220.3% 34.1% 23,045247,8853,226 (57.5)4,148 (74.0)14.0Public
Anonymous submission
MOT_HY
66. using public detections 		58.9
47.3
±121.2
49.417.2% 33.8% 46,875246,0614,231 (75.0)8,188 (145.2)2.0Public
Anonymous submission
OLGT_new
67. online method using public detections 		55.9
45.7
±22.8
49.410.8% 75.5% 6,915298,2881,418 (30.1)3,641 (77.2)6.1Public
Anonymous submission
PHD_GSDL17
68. online method using public detections 		55.4
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.
MOT_TBC
69. using public detections 		36.3
53.9
±15.7
50.020.2% 36.7% 24,584232,6702,945 (50.1)4,612 (78.5)6.7Public
Anonymous submission
JDT
70. online method using public detections new 		51.0
47.4
±12.2
50.116.8% 37.2% 26,910267,3312,760 (52.5)6,211 (118.0)35.1Public
Anonymous submission
TrackerAvg RankMOTA IDF1MTMLFPFNID Sw.FragHzDetector
MFT
71. online method using public detections 		51.3
53.1
±16.6
50.120.4% 39.4% 35,295225,6063,681 (61.3)6,271 (104.5)0.7Public
Anonymous submission
OST
72. using public detections 		50.9
49.7
±14.0
50.417.0% 36.7% 21,811258,6493,077 (56.8)4,339 (80.1)1.7Public
Anonymous submission
MOTF17
73. using public detections new 		41.4
52.0
±13.2
50.520.1% 40.4% 19,222249,4642,293 (41.1)3,297 (59.1)2.2Public
Anonymous submission
JBNOT
74. using public detections 		37.4
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.
PPMOT
75. using public detections 		38.2
52.4
±13.4
50.822.4% 40.0% 20,176246,1582,224 (39.5)2,769 (49.1)2.3Public
Anonymous submission
PointMOT17
76. using public detections 		37.4
52.2
±13.3
50.822.4% 40.0% 22,012245,2772,134 (37.8)2,652 (46.9)2.2Public
Anonymous submission
PP17
77. using public detections new 		37.8
52.4
±13.4
50.822.4% 40.0% 20,183245,9982,215 (39.3)2,752 (48.8)1.9Public
Anonymous submission
MOTPP17
78. using public detections new 		29.7
52.4
±15.4
50.822.4% 40.0% 19,922246,1832,223 (39.4)2,769 (49.1)35.5Public
Anonymous submission
DS_TW_F
79. online method using public detections 		44.9
45.7
±27.0
50.910.8% 75.4% 6,528298,3681,329 (28.2)3,180 (67.5)66.9Public
Anonymous submission
HAM_SADF17
80. online method using public detections 		44.8
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.
TrackerAvg RankMOTA IDF1MTMLFPFNID Sw.FragHzDetector
EDMT17
81. using public detections 		41.7
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.
DGCT
82. using public detections 		25.4
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
PV
83. online method using public detections 		41.3
52.8
±14.1
51.819.7% 34.0% 15,884246,9393,711 (66.0)8,757 (155.7)3.5Public
Anonymous submission
MHT_bLSTM
84. using public detections 		50.7
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.
AM_ADM17
85. online method using public detections 		46.9
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.
UTA
86. online method using public detections 		36.8
53.0
±11.6
52.221.7% 31.5% 24,468238,3562,292 (39.7)6,231 (107.9)5.0Public
Anonymous submission
DeepMP17
87. using public detections 		35.7
50.4
±13.1
52.318.8% 38.7% 22,535255,3561,868 (34.1)3,473 (63.4)7.4Public
Tracktor17
88. online method using public detections 		37.9
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.
TPM
89. using public detections 		31.4
54.2
±13.0
52.622.8% 37.5% 13,739242,7301,824 (32.0)2,472 (43.4)0.8Public
Anonymous submission
TppNoFPN
90. using public detections 		42.6
52.4
±15.3
52.618.5% 37.2% 18,635247,1042,726 (48.5)5,461 (97.2)4.2Public
Anonymous submission
TrackerAvg RankMOTA IDF1MTMLFPFNID Sw.FragHzDetector
MOTDT17
91. online method using public detections 		39.5
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.
ReTracktor
92. using public detections 		30.5
55.1
±14.0
52.821.4% 34.9% 15,489235,6942,119 (36.4)4,725 (81.1)0.8Public
Anonymous submission
2MPT
93. using public detections 		42.5
48.1
±14.2
52.917.4% 39.6% 30,650260,1331,860 (34.5)2,784 (51.7)2.7Public
Anonymous submission
TOPA
94. online method using public detections 		36.5
51.8
±13.5
53.419.6% 33.1% 27,603241,5462,668 (46.7)5,790 (101.2)443.9Public
Anonymous submission
DTBasline
95. online method using public detections 		35.6
51.1
±11.7
53.416.7% 35.5% 20,309253,2452,549 (46.2)5,910 (107.2)22.2Public
Anonymous submission
MOT17ZH
96. online method using public detections 		43.9
51.1
±13.7
53.416.7% 35.5% 20,309253,2452,549 (46.2)5,910 (107.2)3.7Public
Anonymous submission
SNet_pub
97. online method using public detections 		44.0
51.7
±12.0
53.418.0% 33.5% 26,809243,0662,735 (48.0)6,157 (108.2)4.9Public
Anonymous submission
SRPN17
98. online method using public detections 		45.8
51.0
±11.7
53.516.8% 35.1% 21,011252,8082,596 (47.0)5,981 (108.4)4.1Public
Anonymous submission
DEEP_TAMA
99. online method using public detections 		39.7
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.
LSMT
100. online method using public detections 		34.0
51.9
±12.0
53.517.4% 35.0% 18,672250,6622,257 (40.6)5,733 (103.2)8.9Public
Anonymous submission
TrackerAvg RankMOTA IDF1MTMLFPFNID Sw.FragHzDetector
CRF_TRA
101. using public detections 		28.4
53.1
±12.2
53.724.2% 30.7% 27,194234,9912,518 (43.2)4,918 (84.3)1.4Public
Anonymous submission
SMOTe
102. online method using public detections new 		45.8
52.0
±12.1
53.718.6% 33.2% 28,624239,5002,712 (47.1)6,167 (107.2)1.0Public
Anonymous submission
AReid17
103. online method using public detections 		32.5
51.4
±12.2
53.919.2% 32.3% 30,079241,3642,993 (52.3)6,373 (111.4)33.7Public
Anonymous submission
YOONKJ17
104. online method using public detections 		41.0
51.4
±13.5
54.021.2% 37.3% 29,051243,2022,118 (37.2)3,072 (54.0)3.4Public
Anonymous submission
NOTA
105. using public detections 		36.9
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.
jCC
106. using public detections 		35.3
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.
STCG17
107. using public detections 		33.8
51.1
±12.9
54.520.4% 38.6% 32,258241,9161,702 (29.8)2,483 (43.5)66.9Public
Anonymous submission
eHAF17
108. using public detections 		33.2
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.
overlap
109. using public detections 		28.2
51.5
±13.1
55.623.0% 36.1% 38,322233,2751,860 (31.7)2,935 (50.0)66.9Public
Anonymous submission
MOT_AF
110. online method using public detections new 		25.3
53.5
±13.4
55.619.2% 37.6% 12,867247,8161,672 (29.8)3,516 (62.7)25.2Public
Anonymous submission
TrackerAvg RankMOTA IDF1MTMLFPFNID Sw.FragHzDetector
DMAN
111. online method using public detections 		45.8
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.
TLMHT
112. using public detections 		44.0
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.
ENFT17
113. using public detections 		27.8
52.8
±13.1
57.123.1% 36.8% 26,754237,9091,667 (28.8)2,557 (44.2)0.5Public
BUAA
SAS_MOT17
114. using public detections 		56.1
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.
cnt_klt
115. using public detections 		26.2
48.0
±11.5
57.819.0% 31.6% 63,207228,7831,215 (20.4)4,134 (69.5)59.2Public
Anonymous submission
LSST17O
116. online method using public detections 		38.9
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
eTC17
117. using public detections 		32.8
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 arXiv preprint arXiv:1811.07258, 2018.
CMT
118. using public detections 		30.2
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
IDGA
119. using public detections 		24.3
52.6
±13.4
61.323.6% 40.2% 29,049236,8301,402 (24.2)2,613 (45.0)59.2Public
Anonymous submission
LSST17
120. using public detections 		33.0
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
TrackerAvg RankMOTA IDF1MTMLFPFNID Sw.FragHzDetector
TT17
121. using public detections new 		25.7
54.9
±12.8
63.124.4% 38.1% 20,236233,2951,088 (18.5)2,392 (40.8)2.5Public
Anonymous submission
ISDH_HDAv2
122. online method using public detections 		32.9
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

Benchmark Statistics

SequencesFramesTrajectoriesBoxes
21177572355564228

Difficulty Analysis

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

MOT17-03-SDP

MOT17-03-SDP

(70.9% MOTA)

MOT17-03-FRCNN

MOT17-03-FRCNN

(58.0% MOTA)

MOT17-06-SDP

MOT17-06-SDP

(48.4% MOTA)

...

...

MOT17-14-DPM

MOT17-14-DPM

(18.7% MOTA)

MOT17-14-FRCNN

MOT17-14-FRCNN

(17.8% MOTA)

Evaluation Measures

Lower is better. Higher is better.
Measure Better Perfect Description
Avg Rank lower 1 This is the rank of each tracker averaged over all present evaluation measures.
MOTA higher 100 % Multiple Object Tracking Accuracy [1]. This measure combines three error sources: false positives, missed targets and identity switches.
MOTP higher 100 % Multiple Object Tracking Precision [1]. The misalignment between the annotated and the predicted bounding boxes.
IDF1 higher 100 % ID F1 Score [2]. The ratio of correctly identified detections over the average number of ground-truth and computed detections.
FAF lower 0 The average number of false alarms per frame.
MT higher 100 % Mostly tracked targets. The ratio of ground-truth trajectories that are covered by a track hypothesis for at least 80% of their respective life span.
ML lower 0 % Mostly lost targets. The ratio of ground-truth trajectories that are covered by a track hypothesis for at most 20% of their respective life span.
FP lower 0 The total number of false positives.
FN lower 0 The total number of false negatives (missed targets).
ID Sw. lower 0 The total number of identity switches. Please note that we follow the stricter definition of identity switches as described in [3].
Frag lower 0 The total number of times a trajectory is fragmented (i.e. interrupted during tracking).
Hz higher Inf. Processing speed (in frames per second excluding the detector) on the benchmark.

Legend

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

References:


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



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