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!

TrackerAvg RankMOTAIDF1MTMLFPFN ID Sw.FragHzDetector
BIG_HA
1. online method using public detections
57.1
-37.9
±24.1
0.00.0% 100.0% 213,867564,2280 (nan)0 (nan)887.9Public
Anonymous submission
CDT
2. using public detections
58.2
-64.5
±16.9
0.10.0% 99.4% 364,642563,67272 (730.7)64 (649.5)46.9Public
Anonymous submission
LSST17
3. using public detections
29.6
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
CMT
4. using public detections
30.4
50.4
±13.0
58.217.8% 44.3% 25,443253,0961,267 (23.0)2,079 (37.7)10.2Public
Anonymous submission
TLMHT
5. using public detections
39.7
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.
SAS_MOT17
6. using public detections
50.8
44.2
±12.2
57.216.1% 44.3% 29,473283,6111,529 (30.7)2,644 (53.2)4.8Public
Anonymous submission
HCC
7. using public detections
44.7
44.8
±11.2
46.818.3% 38.9% 17,586292,2941,555 (32.3)2,221 (46.1)0.9Public
Anonymous submission
STCG17
8. using public detections
30.7
51.1
±12.9
54.520.4% 38.6% 32,258241,9161,702 (29.8)2,483 (43.5)66.9Public
Anonymous submission
jCC
9. using public detections
31.7
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.
TPM
10. using public detections
29.2
54.2
±13.0
52.622.8% 37.5% 13,739242,7301,824 (32.0)2,472 (43.4)0.8Public
Anonymous submission
TrackerAvg RankMOTAIDF1MTMLFPFN ID Sw.FragHzDetector
eHAF17
11. using public detections
29.6
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
12. using public detections
25.4
51.5
±13.1
55.623.0% 36.1% 38,322233,2751,860 (31.7)2,935 (50.0)66.9Public
Anonymous submission
DeepMP17
13. using public detections
32.2
50.4
±13.1
52.318.8% 38.7% 22,535255,3561,868 (34.1)3,473 (63.4)7.4Public
HAM_SADF17
14. online method using public detections
40.0
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.
HDTR
15. using public detections
22.3
54.1
±11.4
48.423.3% 34.8% 18,002238,8181,895 (32.9)2,693 (46.7)1.8Public
TBNMF17
16. online method using public detections
38.9
50.6
±12.6
49.318.9% 39.2% 17,522258,9902,014 (37.2)4,432 (81.9)6.9Public
Anonymous submission
yt_face
17. online method using public detections
31.9
52.6
±13.1
51.523.0% 35.9% 23,894241,4892,047 (35.8)2,827 (49.4)2.2Public
Anonymous submission
MHT_bLSTM
18. using public detections
45.1
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.
COMOT
19. online method using public detections
45.8
46.4
±13.5
48.514.8% 42.2% 20,752279,8162,069 (41.0)4,606 (91.4)5.0Public
Anonymous submission
SemiOMOT
20. using public detections
32.3
52.4
±15.0
51.022.6% 34.6% 23,660242,9532,070 (36.4)3,170 (55.7)0.7Public
Anonymous submission
TrackerAvg RankMOTAIDF1MTMLFPFN ID Sw.FragHzDetector
Tracktor17
21. online method using public detections
34.4
53.5
±14.5
52.319.5% 36.6% 12,201248,0472,072 (37.0)4,611 (82.3)1.5Public
Anonymous submission
AEb
22. using public detections
35.6
47.9
±13.6
47.018.1% 40.7% 15,828276,1792,082 (40.8)4,733 (92.7)66.9Public
Anonymous submission
SSOMOT
23. online method using public detections
51.8
46.8
±13.1
49.215.3% 39.1% 24,041274,2572,121 (41.3)4,897 (95.3)4.9Public
Anonymous submission
LSST17O
24. online method using public detections
35.9
52.7
±13.3
57.917.9% 36.6% 22,512241,9362,167 (37.9)7,443 (130.3)1.8Public
Anonymous submission
DEEP_TAMA
25. online method using public detections
35.8
50.3
±13.3
53.519.2% 37.5% 25,479252,9962,192 (39.7)3,978 (72.1)1.5Public
for journal submission
DMAN
26. online method using public detections
41.8
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.
AM_ADM17
27. online method using public detections
42.9
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.
EDMT17
28. using public detections
36.9
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.
AEb_O
29. online method using public detections
47.5
46.4
±13.9
44.916.5% 41.4% 17,030283,2652,266 (45.5)5,053 (101.5)1.8Public
Anonymous submission
LM_NN
30. using public detections
51.8
45.1
±13.3
43.214.8% 46.2% 10,834296,4512,286 (48.2)2,463 (51.9)2.5Public
NEUCOM-D-18-03230
TrackerAvg RankMOTAIDF1MTMLFPFN ID Sw.FragHzDetector
eTC17
31. using public detections
29.0
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.
hpmmt17
32. online method using public detections
29.3
51.2
±11.8
53.617.3% 34.9% 21,957250,8912,292 (41.3)6,108 (110.0)44,392.5Public
Anonymous submission
MHT_DAM
33. using public detections
39.1
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.
MOT_test
34. online method using public detections
33.3
51.6
±11.9
53.917.3% 35.5% 21,419249,0592,384 (42.7)5,613 (100.5)7.8Public
Anonymous submission
HIK_MOT17
35. using public detections
23.2
53.9
±13.7
54.323.7% 32.0% 27,656230,0422,386 (40.3)4,192 (70.8)5.4Public
Sn_PBC
36. using public detections
36.2
51.3
±11.7
53.417.4% 35.2% 21,255251,2562,394 (43.2)6,148 (110.8)14.8Public
Anonymous submission
IDGA
37. using public detections
33.5
49.9
±12.2
50.322.1% 36.7% 37,060243,1482,426 (42.6)3,846 (67.6)59.2Public
Anonymous submission
NV_MC
38. using public detections
44.3
49.1
±13.9
45.719.0% 38.0% 16,850267,9232,446 (46.6)3,196 (60.9)0.3Public
Anonymous submission
MOTDT17
39. online method using public detections
35.3
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.
DSA_MOT17
40. online method using public detections
47.3
45.0
±12.6
43.615.8% 39.2% 21,442286,4822,491 (50.6)3,824 (77.7)9.9Public
Anonymous submission
TrackerAvg RankMOTAIDF1MTMLFPFN ID Sw.FragHzDetector
CRF_TRA
41. using public detections
25.5
53.1
±12.1
53.724.2% 30.7% 27,194234,9912,518 (43.2)4,918 (84.3)1.4Public
Anonymous submission
DTBasline
42. online method using public detections
31.9
51.1
±11.7
53.416.7% 35.5% 20,309253,2452,549 (46.2)5,910 (107.2)22.2Public
Anonymous submission
AFN17
43. using public detections
32.0
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.
SRPN17
44. online method using public detections
40.1
51.0
±11.7
53.516.8% 35.1% 21,011252,8082,596 (47.0)5,981 (108.4)4.1Public
Anonymous submission
PA_MOT17
45. online method using public detections
31.3
51.6
±13.5
53.518.9% 33.5% 28,794241,7042,635 (46.1)5,808 (101.6)710.3Public
Anonymous submission
FWT
46. using public detections
36.3
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.
TOPA
47. online method using public detections
32.5
51.8
±13.5
53.419.6% 33.1% 27,603241,5462,668 (46.7)5,790 (101.2)443.9Public
Anonymous submission
CEMT
48. using public detections
40.8
49.3
±12.6
44.416.8% 38.5% 21,711261,8082,696 (50.3)3,409 (63.6)5.8Public
Anonymous submission
DGCT
49. using public detections
22.8
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
MOT_TBC
50. using public detections
33.3
53.9
±15.7
50.020.2% 36.7% 24,584232,6702,945 (50.1)4,612 (78.5)6.7Public
Anonymous submission
TrackerAvg RankMOTAIDF1MTMLFPFN ID Sw.FragHzDetector
NOTBD
51. using public detections
35.5
53.9
±12.7
51.221.5% 35.6% 28,912228,3562,964 (49.8)3,600 (60.5)0.3Public
Anonymous submission
TBD17_1
52. online method using public detections
35.9
51.4
±11.7
52.018.5% 33.2% 24,261247,1952,985 (53.1)6,611 (117.7)1,183.8Public
Anonymous submission
REQT
53. online method using public detections
61.1
43.9
±14.2
47.413.1% 45.8% 34,309279,0302,986 (59.1)5,402 (106.9)64.1Public
Anonymous submission
STDIC
54. online method using public detections
57.8
44.1
±13.6
45.913.2% 39.6% 46,126266,4492,992 (56.7)5,143 (97.4)17,757.0Public
Anonymous submission
AReid17
55. online method using public detections
28.1
51.4
±12.2
53.919.2% 32.3% 30,079241,3642,993 (52.3)6,373 (111.4)33.7Public
Anonymous submission
ISDH_HDAv2
56. online method using public detections
29.4
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
JBNOT
57. using public detections
34.2
52.6
±12.3
50.819.7% 35.8% 31,572232,6593,050 (51.9)3,792 (64.5)5.4Public
Anonymous submission
Tensor17
58. online method using public detections
41.1
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 arXiv:1904.04989, 2019.
L_SORT
59. using public detections
55.3
45.0
±14.0
46.012.2% 41.1% 19,967287,2293,294 (67.1)8,292 (168.9)102.6Public
Anonymous submission
ts_WCFMT
60. online method using public detections
49.3
48.4
±13.6
51.421.0% 32.5% 32,037255,4723,410 (62.3)6,351 (116.1)1.0Public
Anonymous submission
TrackerAvg RankMOTAIDF1MTMLFPFN ID Sw.FragHzDetector
TEM
61. using public detections
48.3
49.1
±12.6
45.417.0% 38.3% 22,119261,7973,439 (64.2)3,881 (72.4)8.2Public
Anonymous submission
RTRC
62. online method using public detections
48.6
48.5
±14.2
48.618.7% 35.7% 34,180252,8593,490 (63.2)6,304 (114.2)9.8Public
Anonymous submission
TCF
63. online method using public detections
50.0
48.3
±13.6
48.718.9% 35.1% 36,274252,0923,530 (63.8)6,390 (115.5)6.4Public
Anonymous submission
WCFMT17
64. using public detections
48.5
47.3
±16.0
52.321.9% 30.7% 43,253250,3023,556 (63.9)6,071 (109.1)1.0Public
Anonymous submission
DCORV2
65. online method using public detections
54.2
45.5
±13.9
36.114.6% 40.4% 21,161282,9013,592 (72.0)7,696 (154.4)35.5Public
Anonymous submission
TAR_1
66. online method using public detections
42.2
51.6
±11.9
41.421.7% 28.7% 33,514235,8593,629 (62.4)5,949 (102.2)5.6Public
Anonymous submission
DeepMOTRPN
67. online method using public detections
49.5
48.1
±14.5
43.017.6% 38.6% 26,490262,5783,696 (69.1)5,353 (100.1)4.9Public
Anonymous submission
PV
68. online method using public detections
36.2
52.8
±14.1
51.819.7% 34.0% 15,884246,9393,711 (66.0)8,757 (155.7)3.5Public
Anonymous submission
PHD_GSDL17
69. online method using public detections
49.9
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.
OST
70. using public detections
45.1
49.2
±14.3
47.618.6% 35.3% 21,844260,8714,022 (74.8)4,821 (89.7)1.7Public
Anonymous submission
TrackerAvg RankMOTAIDF1MTMLFPFN ID Sw.FragHzDetector
EDA_GNN
71. online method using public detections
53.8
45.5
±13.8
40.515.6% 40.6% 25,685277,6634,091 (80.5)5,579 (109.8)39.3Public
Paper ID 2713
TPbase17
72. online method using public detections
62.6
43.3
±15.0
48.216.2% 36.6% 49,992265,8154,194 (79.3)12,103 (228.8)22.2Public
Anonymous submission
RTac
73. online method using public detections
48.9
46.3
±14.6
49.218.9% 33.5% 43,447255,1584,196 (76.6)6,056 (110.6)14.1Public
Anonymous submission
SNM17
74. online method using public detections
63.6
46.8
±13.8
43.416.2% 37.1% 25,104271,0424,213 (81.1)9,891 (190.3)0.8Public
Anonymous submission
QiMOT
75. online method using public detections
59.2
47.2
±13.1
40.815.5% 39.9% 18,907274,8284,320 (84.2)5,917 (115.4)1.8Public
Anonymous submission
PHD_GM
76. online method using public detections
47.1
48.8
±13.4
43.219.1% 35.2% 26,260257,9714,407 (81.2)6,448 (118.8)22.3Public
Anonymous submission
Q_ls
77. online method using public detections
50.6
50.2
±14.4
43.619.7% 37.3% 23,143253,1514,414 (80.1)6,112 (110.9)1.8Public
Anonymous submission
MOT_HY
78. using public detections new
47.1
42.9
±150.7
49.923.8% 23.3% 99,045218,6704,431 (72.3)9,108 (148.7)2.0Public
Anonymous submission
MASS
79. online method using public detections new
54.1
46.9
±14.1
46.016.9% 36.3% 25,733269,1164,478 (85.6)11,994 (229.3)12.3Public
Anonymous submission
EAMTT
80. online method using public detections
63.2
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.
TrackerAvg RankMOTAIDF1MTMLFPFN ID Sw.FragHzDetector
GM_PHD
81. online method using public detections
67.8
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.
Qclc
82. online method using public detections
35.3
54.0
±14.3
47.723.3% 30.7% 22,374232,2124,748 (80.7)6,022 (102.3)1.8Public
Anonymous submission
dcor
83. online method using public detections
57.7
45.0
±14.2
34.015.4% 38.2% 30,231275,2654,801 (93.7)8,498 (165.9)44.4Public
Anonymous submission
MOT_BJ
84. online method using public detections
83.4
-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
GOTURN_3B
85. online method using public detections
60.8
44.3
±13.7
38.513.0% 43.2% 30,302279,1444,861 (96.2)5,277 (104.4)48.6Public
Anonymous submission
YoloSort
86. online method using public detections
60.2
29.5
±24.1
41.715.0% 36.4% 154,747238,2414,888 (84.6)4,952 (85.7)14.4Public
Anonymous submission
PeriodMOT
87. online method using public detections
62.2
43.8
±13.2
40.914.7% 42.0% 21,941290,1944,910 (101.1)6,649 (136.9)66.9Public
Anonymous submission
PHD_LMP
88. online method using public detections
59.7
45.9
±13.1
42.515.5% 37.9% 27,946272,1964,977 (96.2)6,985 (135.0)29.4Public
Anonymous submission
D_SST_V1
89. online method using public detections
66.2
42.7
±13.9
46.111.8% 44.4% 18,861298,9895,531 (117.7)13,775 (293.0)2.3Public
Anonymous submission
MTDF17
90. online method using public detections
52.8
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.
TrackerAvg RankMOTAIDF1MTMLFPFN ID Sw.FragHzDetector
terry_T
91. online method using public detections
80.3
2.5
±6.9
12.10.4% 83.9% 96,372448,0055,756 (279.4)11,270 (547.1)34.7Public
Anonymous submission
IDOHMPT
92. online method using public detections
57.7
46.0
±13.1
44.116.8% 36.6% 30,873268,2215,768 (109.9)9,663 (184.2)8.1Public
Anonymous submission
GMPHD_KCF
93. online method using public detections
73.8
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.
DH_TRK
94. using public detections
30.7
54.1
±13.0
49.221.6% 28.4% 36,196216,6705,918 (96.1)7,760 (126.0)1,775.7Public
Anonymous submission
IOU17
95. using public detections
55.7
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.
reID2track
96. online method using public detections
66.3
44.6
±14.3
39.915.8% 39.7% 22,451284,2136,134 (123.6)13,786 (277.8)9.0Public
Anonymous submission
IOUT_Re
97. online method using public detections
36.5
52.7
±13.0
43.320.1% 32.6% 16,529243,2266,946 (122.1)6,520 (114.6)7.0Public
Anonymous submission
FPSN
98. online method using public detections
57.3
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.
YT_T
99. online method using public detections
62.3
45.4
±13.4
40.316.0% 35.7% 25,425275,0507,652 (149.3)8,249 (160.9)11.4Public
Anonymous submission
ORCtracker
100. online method using public detections
45.2
50.7
±13.7
43.117.0% 35.2% 20,440249,7918,069 (144.8)11,188 (200.8)3,760.7Public
Anonymous submission
TrackerAvg RankMOTAIDF1MTMLFPFN ID Sw.FragHzDetector
zxbtk17
101. online method using public detections new
72.5
44.3
±15.0
29.618.5% 36.8% 33,024272,6498,422 (163.0)12,041 (233.0)1.7Public
Anonymous submission
baitrack
102. using public detections
51.8
37.6
±19.4
20.321.0% 30.9% 99,085244,0018,808 (155.2)6,708 (118.2)6.4Public
Anonymous submission
ZM
103. online method using public detections
71.8
43.5
±13.9
32.614.5% 39.9% 25,083284,4059,197 (185.4)8,849 (178.4)14.4Public
Anonymous submission
BU_CV
104. online method using public detections
61.4
42.8
±14.4
32.315.8% 36.1% 40,573271,83810,118 (195.2)9,426 (181.9)17.8Public
Anonymous submission
GM_PHD
105. online method using public detections
66.5
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
106. online method using public detections
67.3
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.
ReDetPast
107. online method using public detections
68.3
44.3
±14.8
34.917.3% 36.7% 32,113271,34310,962 (211.2)11,733 (226.0)3.3Public
Anonymous submission
GMPHD_DAL
108. online method using public detections
64.8
44.4
±13.9
36.214.9% 39.4% 19,170283,38011,137 (223.7)13,900 (279.3)3.5Public
Anonymous submission
MOTbyReID
109. online method using public detections new
66.7
43.6
±13.7
37.117.6% 36.3% 35,725270,03612,347 (236.8)11,408 (218.8)2.5Public
Anonymous submission
XYHv2
110. online method using public detections
80.1
39.9
±12.4
23.89.9% 41.8% 29,713296,70412,900 (272.1)12,911 (272.3)7.8Public
Anonymous submission
TrackerAvg RankMOTAIDF1MTMLFPFN ID Sw.FragHzDetector
GM_PHD_D
111. online method using public detections
63.8
44.0
±13.8
34.214.8% 39.4% 19,135283,53013,556 (272.5)13,821 (277.8)9.9Public
Anonymous submission
TM_track
112. online method using public detections
80.2
41.1
±14.9
32.813.2% 41.3% 27,606287,51117,408 (355.0)15,197 (309.9)2.5Public
Anonymous submission

Benchmark Statistics

SequencesFramesTrajectoriesBoxes
21177572355564228

Difficulty Analysis

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

MOT17-03-SDP

MOT17-03-SDP

(69.2% MOTA)

MOT17-03-FRCNN

MOT17-03-FRCNN

(55.9% MOTA)

MOT17-06-SDP

MOT17-06-SDP

(47.8% MOTA)

...

...

MOT17-14-DPM

MOT17-14-DPM

(18.0% MOTA)

MOT17-14-FRCNN

MOT17-14-FRCNN

(17.4% 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.