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 RankMOTAIDF1MTMLFPFNID Sw.Frag HzDetector
IOU17
1. using public detections
44.4
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.
TOPA
2. online method using public detections
26.8
51.8
±13.5
53.419.6% 33.1% 27,603241,5462,668 (46.7)5,790 (101.2)443.9Public
Anonymous submission
SOTD_MC
3. online method using public detections
34.8
48.4
±15.0
45.519.4% 35.9% 33,525255,0912,531 (46.2)4,944 (90.2)67.0Public
Anonymous submission
overlap
4. using public detections
20.7
51.5
±13.1
55.623.0% 36.1% 38,322233,2751,860 (31.7)2,935 (50.0)66.9Public
Anonymous submission
AEb
5. using public detections
27.8
47.9
±13.6
47.018.1% 40.7% 15,828276,1792,082 (40.8)4,733 (92.7)66.9Public
Anonymous submission
STCG17
6. using public detections
24.4
51.1
±12.9
54.520.4% 38.6% 32,258241,9161,702 (29.8)2,483 (43.5)66.9Public
Anonymous submission
DS_TW_F
7. online method using public detections
33.2
45.7
±27.0
50.910.8% 75.4% 6,528298,3681,329 (28.2)3,180 (67.5)66.9Public
Anonymous submission
GOTURN_3B
8. online method using public detections
48.8
44.3
±13.7
38.513.0% 43.2% 30,302279,1444,861 (96.2)5,277 (104.4)48.6Public
Anonymous submission
dcor
9. online method using public detections
45.4
45.0
±14.2
34.015.4% 38.2% 30,231275,2654,801 (93.7)8,498 (165.9)44.4Public
Anonymous submission
EDA_GNN
10. online method using public detections
42.2
45.5
±13.8
40.515.6% 40.6% 25,685277,6634,091 (80.5)5,579 (109.8)39.3Public
Paper ID 2713
TrackerAvg RankMOTAIDF1MTMLFPFNID Sw.Frag HzDetector
GM_PHD
11. online method using public detections
55.8
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.
DCORV2
12. online method using public detections
42.7
45.5
±13.9
36.114.6% 40.4% 21,161282,9013,592 (72.0)7,696 (154.4)35.5Public
Anonymous submission
AReid17
13. online method using public detections
23.3
51.4
±12.2
53.919.2% 32.3% 30,079241,3642,993 (52.3)6,373 (111.4)33.7Public
Anonymous submission
PHD_LMP
14. online method using public detections
47.3
45.9
±13.1
42.515.5% 37.9% 27,946272,1964,977 (96.2)6,985 (135.0)29.4Public
Anonymous submission
PHD_GM
15. online method using public detections
38.0
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.
DTBasline
16. online method using public detections
25.8
51.1
±11.7
53.416.7% 35.5% 20,309253,2452,549 (46.2)5,910 (107.2)22.2Public
Anonymous submission
TPbase17
17. online method using public detections
49.4
43.3
±15.0
48.216.2% 36.6% 49,992265,8154,194 (79.3)12,103 (228.8)22.2Public
Anonymous submission
c3d_Track
18. online method using public detections
53.2
41.5
±13.7
40.210.7% 48.5% 33,332292,9313,890 (80.9)11,454 (238.2)22.2Public
Anonymous submission
MOTDT17
19. online method using public detections
28.6
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.
NOTA
20. using public detections
26.7
51.3
±11.7
54.517.1% 35.4% 20,148252,5312,285 (41.4)5,798 (105.0)17.8Public
BMVC 2019 Submition 298
TrackerAvg RankMOTAIDF1MTMLFPFNID Sw.Frag HzDetector
MASS
21. online method using public detections
42.7
46.9
±14.1
46.016.9% 36.3% 25,733269,1164,478 (85.6)11,994 (229.3)17.1Public
Anonymous submission
YoloSort
22. online method using public detections
48.1
29.5
±24.1
41.715.0% 36.4% 154,747238,2414,888 (84.6)4,952 (85.7)14.4Public
Anonymous submission
ZM
23. online method using public detections
56.5
43.5
±13.9
32.614.5% 39.9% 25,083284,4059,197 (185.4)8,849 (178.4)14.4Public
Anonymous submission
EAMTT
24. online method using public detections
50.0
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.
CGHA_MOT
25. online method using public detections
54.5
41.2
±14.1
44.08.3% 45.9% 25,462299,1127,294 (155.2)18,655 (397.0)11.4Public
Anonymous submission
CASC_MOT
26. online method using public detections new
49.1
42.3
±12.8
46.89.1% 44.1% 21,035300,7973,616 (77.4)16,656 (356.7)11.4Public
Anonymous submission
FPSN
27. online method using public detections
45.9
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.
GM_PHD
28. online method using public detections
53.1
42.1
±13.0
33.911.9% 42.7% 18,214297,64610,698 (226.4)10,864 (229.9)9.9Public
Anonymous submission
DSA_MOT17
29. online method using public detections
37.1
45.0
±12.6
43.615.8% 39.2% 21,442286,4822,491 (50.6)3,824 (77.7)9.9Public
Anonymous submission
GM_PHD_D
30. online method using public detections
50.8
44.0
±13.8
34.214.8% 39.4% 19,135283,53013,556 (272.5)13,821 (277.8)9.9Public
Anonymous submission
TrackerAvg RankMOTAIDF1MTMLFPFNID Sw.Frag HzDetector
GMPHD_N1Tr
31. online method using public detections
53.8
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.
GMPHD_SHA
32. online method using public detections
49.3
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.
LSMT
33. online method using public detections
24.3
51.9
±12.0
53.517.4% 35.0% 18,672250,6622,257 (40.6)5,733 (103.2)8.9Public
Anonymous submission
zxbtk17
34. online method using public detections
46.9
44.8
±14.8
44.317.8% 31.6% 33,339273,7924,527 (87.9)8,589 (166.9)8.3Public
Anonymous submission
XYHv2
35. online method using public detections
64.2
39.9
±12.4
23.89.9% 41.8% 29,713296,70412,900 (272.1)12,911 (272.3)7.8Public
Anonymous submission
DeepMP17
36. using public detections
26.0
50.4
±13.1
52.318.8% 38.7% 22,535255,3561,868 (34.1)3,473 (63.4)7.4Public
LT17
37. online method using public detections new
51.6
43.2
±15.9
42.915.4% 37.2% 55,835259,8125,102 (94.6)8,979 (166.4)7.2Public
Anonymous submission
DGCT
38. using public detections
18.4
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
PHD_GSDL17
39. online method using public detections
40.8
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
40. using public detections
26.9
53.9
±15.7
50.020.2% 36.7% 24,584232,6702,945 (50.1)4,612 (78.5)6.7Public
Anonymous submission
TrackerAvg RankMOTAIDF1MTMLFPFNID Sw.Frag HzDetector
CMT
41. using public detections
21.2
51.8
±12.9
60.719.6% 42.8% 29,528240,9601,217 (21.2)2,008 (35.0)6.5Public
#Submission: TCSVT-02964-2019
baitrack
42. using public detections
41.8
37.6
±19.4
20.321.0% 30.9% 99,085244,0018,808 (155.2)6,708 (118.2)6.4Public
Anonymous submission
OLGT_new
43. online method using public detections
40.9
45.7
±22.8
49.410.8% 75.5% 6,915298,2881,418 (30.1)3,641 (77.2)6.1Public
Anonymous submission
AM_ADM17
44. online method using public detections
34.3
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.
TAR_1
45. online method using public detections
34.3
51.6
±11.9
41.421.7% 28.7% 33,514235,8593,629 (62.4)5,949 (102.2)5.6Public
Anonymous submission
JBNOT
46. using public detections
27.4
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.
UTA
47. online method using public detections new
27.2
53.0
±11.6
52.221.7% 31.5% 24,468238,3562,292 (39.7)6,231 (107.9)5.0Public
Anonymous submission
HAM_SADF17
48. online method using public detections
33.0
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.
DeepMOTRPN
49. online method using public detections
38.8
48.1
±14.5
43.017.6% 38.6% 26,490262,5783,696 (69.1)5,353 (100.1)4.9Public
Anonymous submission
GLMBS3
50. using public detections new
58.7
38.0
±13.7
32.39.3% 52.8% 38,874304,0166,963 (151.0)3,927 (85.2)4.9Public
Anonymous submission
TrackerAvg RankMOTAIDF1MTMLFPFNID Sw.Frag HzDetector
SAS_MOT17
51. using public detections
40.3
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.
SiaIOU
52. online method using public detections
48.3
46.5
±13.3
40.115.5% 40.4% 13,923282,5345,356 (107.3)5,855 (117.3)4.5Public
Anonymous submission
TppNoFPN
53. using public detections
31.6
52.4
±15.3
52.618.5% 37.2% 18,635247,1042,726 (48.5)5,461 (97.2)4.2Public
Anonymous submission
SRPN17
54. online method using public detections
33.4
51.0
±11.7
53.516.8% 35.1% 21,011252,8082,596 (47.0)5,981 (108.4)4.1Public
Anonymous submission
ms_dh
55. online method using public detections
59.6
42.6
±14.6
40.113.6% 40.0% 31,878284,5287,446 (150.2)14,736 (297.3)4.0Public
Anonymous submission
MOT17ZH
56. online method using public detections
32.6
51.1
±13.7
53.416.7% 35.5% 20,309253,2452,549 (46.2)5,910 (107.2)3.7Public
Anonymous submission
ISDH_HDAv2
57. online method using public detections
24.8
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
PV
58. online method using public detections
30.5
52.8
±14.1
51.819.7% 34.0% 15,884246,9393,711 (66.0)8,757 (155.7)3.5Public
Anonymous submission
GMPHD_DAL
59. online method using public detections
52.1
44.4
±13.9
36.214.9% 39.4% 19,170283,38011,137 (223.7)13,900 (279.3)3.5Public
https://www.researchgate.net/publication/333521185_Online_Multi-object_Visual_Tracking_using_a_GM-PHD_Filter_with_Deep_Appearance_Learning
GMPHD_KCF
60. online method using public detections
60.6
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.
TrackerAvg RankMOTAIDF1MTMLFPFNID Sw.Frag HzDetector
2MPT
61. using public detections
30.8
48.1
±14.2
52.917.4% 39.6% 30,650260,1331,860 (34.5)2,784 (51.7)2.7Public
Anonymous submission
TLMHT
62. using public detections
32.0
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.
TM_track
63. online method using public detections
64.3
41.1
±14.9
32.813.2% 41.3% 27,606287,51117,408 (355.0)15,197 (309.9)2.5Public
Anonymous submission
MOTbyReID
64. online method using public detections
53.8
43.6
±13.7
37.117.6% 36.3% 35,725270,03612,347 (236.8)11,408 (218.8)2.5Public
Anonymous submission
D_SST_V1
65. online method using public detections
53.5
42.7
±13.9
46.111.8% 44.4% 18,861298,9895,531 (117.7)13,775 (293.0)2.3Public
Anonymous submission
MOT_HY
66. using public detections
42.8
47.3
±121.2
49.417.2% 33.8% 46,875246,0614,231 (75.0)8,188 (145.2)2.0Public
Anonymous submission
MHT_bLSTM
67. using public detections
37.2
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.
Q_ls
68. online method using public detections
41.5
50.2
±14.4
43.619.7% 37.3% 23,143253,1514,414 (80.1)6,112 (110.9)1.8Public
Anonymous submission
QiMOT
69. online method using public detections
48.1
47.2
±13.1
40.815.5% 39.9% 18,907274,8284,320 (84.2)5,917 (115.4)1.8Public
Anonymous submission
LSST17O
70. online method using public detections
29.1
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
TrackerAvg RankMOTAIDF1MTMLFPFNID Sw.Frag HzDetector
jCC
71. using public detections
25.9
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.
AFN17
72. using public detections
26.0
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.
HDTR
73. using public detections
19.0
54.1
±11.4
48.423.3% 34.8% 18,002238,8181,895 (32.9)2,693 (46.7)1.8Public
AEb_O
74. online method using public detections
38.2
46.4
±13.9
44.916.5% 41.4% 17,030283,2652,266 (45.5)5,053 (101.5)1.8Public
Anonymous submission
OST
75. using public detections
38.2
49.7
±14.0
50.417.0% 36.7% 21,811258,6493,077 (56.8)4,339 (80.1)1.7Public
Anonymous submission
DEEP_TAMA
76. online method using public detections
29.3
50.3
±13.3
53.519.2% 37.5% 25,479252,9962,192 (39.7)3,978 (72.1)1.5Public
Young-Chul Yoon, Online Multiple Pedestrian Tracking with Deep Temporal Appearance Matching Association, Journal submission
Tracktor17
77. online method using public detections
28.3
53.5
±14.5
52.319.5% 36.6% 12,201248,0472,072 (37.0)4,611 (82.3)1.5Public
Anonymous submission
LSST17
78. using public detections
23.8
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
CRF_TRA
79. using public detections new
20.7
53.1
±12.2
53.724.2% 30.7% 27,194234,9912,518 (43.2)4,918 (84.3)1.4Public
Anonymous submission
MTDF17
80. online method using public detections
44.3
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 RankMOTAIDF1MTMLFPFNID Sw.Frag HzDetector
MHT_DAM
81. using public detections
31.9
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.
TPM
82. using public detections
23.8
54.2
±13.0
52.622.8% 37.5% 13,739242,7301,824 (32.0)2,472 (43.4)0.8Public
Anonymous submission
eHAF17
83. using public detections
24.3
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.
eTC17
84. using public detections
24.0
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.
MFT
85. online method using public detections
37.6
53.1
±16.6
50.120.4% 39.4% 35,295225,6063,681 (61.3)6,271 (104.5)0.7Public
Anonymous submission
EDMT17
86. using public detections
30.6
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.
ENFT17
87. using public detections new
23.4
52.3
±13.3
54.522.5% 36.4% 28,743238,2681,959 (33.9)2,973 (51.5)0.5Public
BUAA
DMAN
88. online method using public detections
33.8
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.
FWT
89. using public detections
29.5
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.
Tensor17
90. online method using public detections
33.8
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.
TrackerAvg RankMOTAIDF1MTMLFPFNID Sw.Frag HzDetector
MOT_BJ
91. online method using public detections
69.3
-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

Benchmark Statistics

SequencesFramesTrajectoriesBoxes
21177572355564228

Difficulty Analysis

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

MOT17-03-SDP

MOT17-03-SDP

(70.5% MOTA)

MOT17-03-FRCNN

MOT17-03-FRCNN

(57.5% MOTA)

MOT17-03-DPM

MOT17-03-DPM

(47.2% MOTA)

...

...

MOT17-14-DPM

MOT17-14-DPM

(17.5% MOTA)

MOT17-14-FRCNN

MOT17-14-FRCNN

(17.0% 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.