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 RankMOTAIDF1MTMLFP FNID Sw.FragHzDetector
ISDH_HDAv2
1. 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
DH_TRK
2. 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
NOTBD
3. 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
LSST17
4. 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
HIK_MOT17
5. 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
eTC17
6. 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
Qclc
7. 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
CRF_TRA
8. 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
TAR_1
9. 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
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 RankMOTAIDF1MTMLFP FNID Sw.FragHzDetector
CMT
11. 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
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
yt_face
13. 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
TOPA
14. 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
PA_MOT17
15. 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
MTDF17
16. 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
LSST17O
17. 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
TPM
18. 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
SemiOMOT
19. 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
DGCT
20. 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
TrackerAvg RankMOTAIDF1MTMLFP FNID Sw.FragHzDetector
IDGA
21. 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
IOUT_Re
22. 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
BnW
23. 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
MOT_BJ
24. 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
TBD17_1
25. 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
EDMT17
26. 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.
jCC
27. 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.
FWT
28. 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.
AFN17
29. 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
MOT_test
30. 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
TrackerAvg RankMOTAIDF1MTMLFP FNID Sw.FragHzDetector
ORCtracker
31. 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
WCFMT17
32. 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
MOTDT17
33. 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.
hpmmt17
34. 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
Sn_PBC
35. 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
TCF
36. 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
SRPN17
37. 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
RTRC
38. 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
MHT_DAM
39. 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.
DEEP_TAMA
40. 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 RankMOTAIDF1MTMLFP FNID Sw.FragHzDetector
Q_ls
41. 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
DTBasline
42. 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
TLMHT
43. 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.
RTac
44. 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
ts_WCFMT
45. 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
PHD_GM
46. 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
PV
47. 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
TBNMF17
48. 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
TEM
49. 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
CEMT
50. 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
TrackerAvg RankMOTAIDF1MTMLFP FNID Sw.FragHzDetector
DMAN
51. 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.
AM_ADM17
52. 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.
TPbase17
53. 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
PHD_GSDL17
54. 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.
STDIC
55. 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
NV_MC
56. 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
MHT_bLSTM
57. 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.
IDOHMPT
58. 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
HAM_SADF17
59. 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.
FPSN
60. 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.
TrackerAvg RankMOTAIDF1MTMLFP FNID Sw.FragHzDetector
SNM17
61. 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
ReDetPast
62. 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
BU_CV
63. 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
PHD_LMP
64. 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
AEb_Exp_6
65. 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
AEb
66. 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
SSOMOT
67. 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
QiMOT
68. 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
YT_T
69. 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
dcor
70. 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
TrackerAvg RankMOTAIDF1MTMLFP FNID Sw.FragHzDetector
EDA_GNN
71. 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
REQT
72. 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
COMOT
73. 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
IOU17
74. 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.
SAS_MOT17
75. 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
reID2track
76. 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
GMPHD_KCF
77. 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.
ZM
78. 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
DSA_MOT17
79. 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
L_SORT
80. 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
TrackerAvg RankMOTAIDF1MTMLFP FNID Sw.FragHzDetector
TM_track
81. 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
EAMTT
82. 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.
PeriodMOT
83. 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
HCC
84. 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
LM_NN
85. 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
XYHv2
86. 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
GM_PHD
87. 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
AEb_Exp_4
88. 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
GM_PHD
89. 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.
terry_T
90. 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
TrackerAvg RankMOTAIDF1MTMLFP FNID Sw.FragHzDetector
CDT
91. 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
BIG_HA
92. 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

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.