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
47.1
-37.9
±24.1
0.00.0% 100.0% 213,867564,2280 (nan)0 (nan)887.9Public
Anonymous submission
CDT
2. using public detections
48.3
-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
24.8
54.7
±12.9
62.320.4% 40.1% 26,091228,4341,243 (20.9)3,726 (62.6)1.5Public
Anonymous submission
TLMHT
4. using public detections
32.8
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
5. using public detections
42.0
44.2
±12.2
57.216.1% 44.3% 29,473283,6111,529 (30.7)2,644 (53.2)4.8Public
Anonymous submission
HCC
6. using public detections
37.0
44.8
±11.2
46.818.3% 38.9% 17,586292,2941,555 (32.3)2,221 (46.1)0.9Public
Anonymous submission
jCC
7. using public detections
26.3
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.
TPM
8. using public detections
24.2
54.2
±13.0
52.622.8% 37.5% 13,739242,7301,824 (32.0)2,472 (43.4)0.8Public
Anonymous submission
CMT
9. using public detections
18.8
52.0
±13.2
54.823.3% 37.7% 31,660237,5471,827 (31.6)2,738 (47.3)10.2Public
Anonymous submission
eHAF17
10. using public detections
24.8
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.
TrackerAvg RankMOTAIDF1MTMLFPFN ID Sw.FragHzDetector
HAM_SADF17
11. online method using public detections
33.5
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
12. using public detections
18.7
54.1
±11.4
48.423.3% 34.8% 18,002238,8181,895 (32.9)2,693 (46.7)1.8Public
TBNMF17
13. online method using public detections
32.5
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
14. online method using public detections
26.6
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
15. using public detections
37.6
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
16. online method using public detections
38.8
46.4
±13.5
48.514.8% 42.2% 20,752279,8162,069 (41.0)4,606 (91.4)5.0Public
Anonymous submission
SemiOMOT
17. using public detections
26.8
52.4
±15.0
51.022.6% 34.6% 23,660242,9532,070 (36.4)3,170 (55.7)0.7Public
Anonymous submission
SSOMOT
18. online method using public detections
43.5
46.8
±13.1
49.215.3% 39.1% 24,041274,2572,121 (41.3)4,897 (95.3)4.9Public
Anonymous submission
LSST17O
19. online method using public detections
29.7
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
20. online method using public detections
29.7
50.3
±13.3
53.519.2% 37.5% 25,479252,9962,192 (39.7)3,978 (72.1)1.5Public
for journal submission
TrackerAvg RankMOTAIDF1MTMLFPFN ID Sw.FragHzDetector
DMAN
21. online method using public detections
34.8
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.
AEb_Exp_4
22. using public detections
44.3
38.6
±16.8
39.314.8% 46.4% 16,841327,2172,206 (52.5)6,959 (165.7)66.9Public
Anonymous submission
AM_ADM17
23. online method using public detections
36.1
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
24. using public detections
31.3
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.
LM_NN
25. using public detections
43.3
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
hpmmt17
26. online method using public detections
24.2
51.2
±11.8
53.617.3% 34.9% 21,957250,8912,292 (41.3)6,108 (110.0)44,392.5Public
Anonymous submission
eTC17
27. using public detections
24.3
51.9
±12.8
58.023.5% 35.5% 37,311231,6582,294 (38.9)2,917 (49.5)0.7Public
Anonymous submission
MHT_DAM
28. using public detections
32.7
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.
AEb
29. using public detections
30.9
48.1
±13.4
46.017.7% 39.5% 16,839273,8192,350 (45.7)5,275 (102.5)66.9Public
Anonymous submission
AEb_Exp_6
30. using public detections new
31.3
48.1
±13.5
45.918.1% 39.5% 17,371273,1172,352 (45.6)4,994 (96.8)66.9Public
Anonymous submission
TrackerAvg RankMOTAIDF1MTMLFPFN ID Sw.FragHzDetector
MOT_test
31. online method using public detections
27.7
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
32. using public detections
19.6
53.9
±13.7
54.323.7% 32.0% 27,656230,0422,386 (40.3)4,192 (70.8)5.4Public
Sn_PBC
33. using public detections
30.8
51.3
±11.7
53.417.4% 35.2% 21,255251,2562,394 (43.2)6,148 (110.8)14.8Public
Anonymous submission
IDGA
34. using public detections
28.6
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
35. using public detections
37.1
49.1
±13.9
45.719.0% 38.0% 16,850267,9232,446 (46.6)3,196 (60.9)0.3Public
Anonymous submission
MOTDT17
36. online method using public detections
29.9
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
37. online method using public detections
40.3
45.0
±12.6
43.615.8% 39.2% 21,442286,4822,491 (50.6)3,824 (77.7)9.9Public
Anonymous submission
CRF_TRA
38. using public detections
21.0
53.1
±12.1
53.724.2% 30.7% 27,194234,9912,518 (43.2)4,918 (84.3)1.8Public
Anonymous submission
DTBasline
39. online method using public detections
27.2
51.1
±11.7
53.416.7% 35.5% 20,309253,2452,549 (46.2)5,910 (107.2)22.2Public
Anonymous submission
AFN17
40. using public detections
27.6
51.5
±13.0
46.920.6% 35.5% 22,391248,4202,593 (46.3)4,308 (77.0)1.8Public
Paper ID 4411
TrackerAvg RankMOTAIDF1MTMLFPFN ID Sw.FragHzDetector
SRPN17
41. online method using public detections
33.8
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
42. online method using public detections
26.0
51.6
±13.5
53.518.9% 33.5% 28,794241,7042,635 (46.1)5,808 (101.6)710.3Public
Anonymous submission
FWT
43. using public detections
31.2
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
44. online method using public detections
27.3
51.8
±13.5
53.419.6% 33.1% 27,603241,5462,668 (46.7)5,790 (101.2)443.9Public
Anonymous submission
CEMT
45. using public detections
34.9
49.3
±12.6
44.416.8% 38.5% 21,711261,8082,696 (50.3)3,409 (63.6)5.8Public
Anonymous submission
BnW
46. online method using public detections
26.3
52.5
±15.3
52.618.7% 36.7% 19,192245,7652,822 (50.0)5,610 (99.4)2.5Public
Anonymous submission
DGCT
47. using public detections
19.8
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
NOTBD
48. using public detections
30.4
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
49. online method using public detections
30.5
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
50. online method using public detections
51.7
43.9
±14.2
47.413.1% 45.8% 34,309279,0302,986 (59.1)5,402 (106.9)64.1Public
Anonymous submission
TrackerAvg RankMOTAIDF1MTMLFPFN ID Sw.FragHzDetector
STDIC
51. online method using public detections
49.0
44.1
±13.6
45.913.2% 39.6% 46,126266,4492,992 (56.7)5,143 (97.4)17,757.0Public
Anonymous submission
ISDH_HDAv2
52. online method using public detections
25.1
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
L_SORT
53. using public detections
46.8
45.0
±14.0
46.012.2% 41.1% 19,967287,2293,294 (67.1)8,292 (168.9)102.6Public
Anonymous submission
MOT_BJ
54. online method using public detections
39.5
50.4
±12.3
51.018.2% 34.1% 30,911245,8313,296 (58.4)6,279 (111.3)0.0Public
Anonymous submission
ts_WCFMT
55. online method using public detections
41.8
48.4
±13.6
51.421.0% 32.5% 32,037255,4723,410 (62.3)6,351 (116.1)1.0Public
Anonymous submission
TEM
56. using public detections
41.2
49.1
±12.6
45.417.0% 38.3% 22,119261,7973,439 (64.2)3,881 (72.4)8.2Public
Anonymous submission
RTRC
57. online method using public detections
41.4
48.5
±14.2
48.618.7% 35.7% 34,180252,8593,490 (63.2)6,304 (114.2)9.8Public
Anonymous submission
TCF
58. online method using public detections
42.4
48.3
±13.6
48.718.9% 35.1% 36,274252,0923,530 (63.8)6,390 (115.5)6.4Public
Anonymous submission
WCFMT17
59. using public detections
41.3
47.3
±16.0
52.321.9% 30.7% 43,253250,3023,556 (63.9)6,071 (109.1)1.0Public
Anonymous submission
TAR_1
60. online method using public detections
35.8
51.6
±11.9
41.421.7% 28.7% 33,514235,8593,629 (62.4)5,949 (102.2)5.6Public
Anonymous submission
TrackerAvg RankMOTAIDF1MTMLFPFN ID Sw.FragHzDetector
PHD_GSDL17
61. online method using public detections
42.4
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.
EDA_GNN
62. online method using public detections
45.8
45.5
±13.8
40.515.6% 40.6% 25,685277,6634,091 (80.5)5,579 (109.8)39.3Public
Paper ID 2713
PV
63. online method using public detections
43.0
48.5
±14.5
48.618.2% 34.9% 27,889258,6894,173 (77.1)8,661 (159.9)3.5Public
Anonymous submission
TPbase17
64. online method using public detections
53.0
43.3
±15.0
48.216.2% 36.6% 49,992265,8154,194 (79.3)12,103 (228.8)22.2Public
Anonymous submission
RTac
65. online method using public detections
41.4
46.3
±14.6
49.218.9% 33.5% 43,447255,1584,196 (76.6)6,056 (110.6)14.1Public
Anonymous submission
SNM17
66. online method using public detections
53.7
46.8
±13.8
43.416.2% 37.1% 25,104271,0424,213 (81.1)9,891 (190.3)0.8Public
Anonymous submission
QiMOT
67. online method using public detections
50.1
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
68. online method using public detections
39.9
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
69. online method using public detections
42.5
50.2
±14.4
43.619.7% 37.3% 23,143253,1514,414 (80.1)6,112 (110.9)1.8Public
Anonymous submission
EAMTT
70. online method using public detections
53.1
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
71. online method using public detections
57.1
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
72. online method using public detections
29.9
54.0
±14.3
47.723.3% 30.7% 22,374232,2124,748 (80.7)6,022 (102.3)1.8Public
Anonymous submission
dcor
73. online method using public detections
49.8
45.0
±14.2
34.015.4% 38.2% 30,231275,2654,801 (93.7)8,498 (165.9)44.4Public
Anonymous submission
PeriodMOT
74. online method using public detections
52.2
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
75. online method using public detections
50.8
45.9
±13.1
42.515.5% 37.9% 27,946272,1964,977 (96.2)6,985 (135.0)29.4Public
Anonymous submission
MTDF17
76. online method using public detections
43.8
49.6
±13.9
45.218.9% 33.1% 37,124241,7685,567 (97.4)9,260 (162.0)1.2Public
Anonymous submission
terry_T
77. online method using public detections
67.1
2.5
±6.9
12.10.4% 83.9% 96,372448,0055,756 (279.4)11,270 (547.1)34.7Public
Anonymous submission
IDOHMPT
78. online method using public detections
48.8
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
79. online method using public detections
61.8
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
80. using public detections
26.3
54.1
±13.0
49.221.6% 28.4% 36,196216,6705,918 (96.1)7,760 (126.0)1,775.7Public
Anonymous submission
TrackerAvg RankMOTAIDF1MTMLFPFN ID Sw.FragHzDetector
IOU17
81. using public detections
47.4
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
82. online method using public detections
55.8
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
83. online method using public detections
30.7
52.7
±13.0
43.320.1% 32.6% 16,529243,2266,946 (122.1)6,520 (114.6)7.0Public
Anonymous submission
FPSN
84. online method using public detections
48.9
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
85. online method using public detections
53.3
45.4
±13.4
40.316.0% 35.7% 25,425275,0507,652 (149.3)8,249 (160.9)11.4Public
Anonymous submission
ORCtracker
86. online method using public detections
38.3
50.7
±13.7
43.117.0% 35.2% 20,440249,7918,069 (144.8)11,188 (200.8)3,760.7Public
Anonymous submission
ZM
87. online method using public detections
60.3
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
88. online method using public detections
52.1
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
89. online method using public detections
56.1
42.1
±13.0
33.911.9% 42.7% 18,214297,64610,698 (226.4)10,864 (229.9)9.9Public
Anonymous submission
ReDetPast
90. online method using public detections
57.1
44.3
±14.8
34.917.3% 36.7% 32,113271,34310,962 (211.2)11,733 (226.0)3.3Public
Anonymous submission
TrackerAvg RankMOTAIDF1MTMLFPFN ID Sw.FragHzDetector
XYHv2
91. online method using public detections
64.3
39.9
±12.4
23.89.9% 41.8% 29,713296,70412,900 (272.1)12,911 (272.3)66.9Public
Anonymous submission
TM_track
92. online method using public detections
66.3
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

(69.8% MOTA)

MOT17-03-FRCNN

MOT17-03-FRCNN

(56.4% MOTA)

MOT17-06-SDP

MOT17-06-SDP

(49.9% MOTA)

...

...

MOT17-14-DPM

MOT17-14-DPM

(20.4% MOTA)

MOT17-14-FRCNN

MOT17-14-FRCNN

(19.9% 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.