2D MOT 2015 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. FragHzDetector
TO
1. using public detections
38.0
25.7
±13.5
32.74.3% 57.4% 4,77940,511383 (11.2)600 (17.6)5.0Public
S. Manen, R. Timofte, D. Dai, L. Gool. Leveraging single for multi-target tracking using a novel trajectory overlap affinity measure. In 2016 IEEE Winter Conference on Applications of Computer Vision (WACV), 2016.
SegTrack
2. using public detections
45.3
22.5
±15.2
31.55.8% 63.9% 7,89039,020697 (19.1)737 (20.2)0.2Public
A. Milan, L. Leal-Taixé, K. Schindler, I. Reid. Joint Tracking and Segmentation of Multiple Targets. In CVPR, 2015.
LINF1
3. using public detections
33.2
24.5
±15.4
34.85.5% 64.6% 5,86440,207298 (8.6)744 (21.5)7.5Public
L. Fagot-Bouquet, R. Audigier, Y. Dhome, F. Lerasle. Improving Multi-Frame Data Association with Sparse Representations for Robust Near-Online Multi-Object Tracking. In ECCV, 2016.
TFMOT
4. online method using public detections
38.5
23.8
±11.9
32.34.9% 62.0% 4,53341,873404 (12.7)792 (24.9)11.3Public
Joint Cost Minimization for Multi-Object Tracking
JCM_MOT
5. online method using public detections
35.3
23.8
±12.0
32.34.9% 62.0% 4,53341,873404 (12.7)792 (24.9)11.3Public
Joint Cost Minimization for Multi-Object Tracking
HAF
6. using public detections
20.3
33.0
±17.8
47.416.4% 44.9% 9,59331,204376 (7.6)804 (16.3)0.6Public
Anonymous submission
DCO_X
7. using public detections
42.3
19.6
±14.1
31.55.1% 54.9% 10,65238,232521 (13.8)819 (21.7)0.3Public
A. Milan, K. Schindler, S. Roth. Multi-Target Tracking by Discrete-Continuous Energy Minimization. In IEEE PAMI, 2016.
NOMT
8. using public detections
19.8
33.7
±16.2
44.612.2% 44.0% 7,76232,547442 (9.4)823 (17.5)11.5Public
W. Choi. Near-Online Multi-target Tracking with Aggregated Local Flow Descriptor. In ICCV, 2015.
MHT_DAM
9. using public detections
22.8
32.4
±15.6
45.316.0% 43.8% 9,06432,060435 (9.1)826 (17.3)0.7Public
C. Kim, F. Li, A. Ciptadi, J. Rehg. Multiple Hypothesis Tracking Revisited. In ICCV, 2015.
LP_SSVM
10. using public detections
33.3
25.2
±13.7
34.05.8% 53.0% 8,36936,932646 (16.2)849 (21.3)41.3Public
S. Wang, C. Fowlkes. Learning Optimal Parameters for Multi-target Tracking with Contextual Interactions. In International Journal of Computer Vision, 2016.
TrackerAvg RankMOTAIDF1MTMLFPFNID Sw. FragHzDetector
EAGS
11. using public detections
13.7
34.2
±15.2
47.516.4% 45.6% 8,73531,304372 (7.6)865 (17.6)192.8Public
#PR-D-17-01373# Enhancing Association Graph with Super-voxel for Multi-target Tracking
JPDA_m
12. using public detections
33.3
23.8
±15.1
33.85.0% 58.1% 6,37340,084365 (10.5)869 (25.0)32.6Public
H. Rezatofighi, A. Milan, Z. Zhang, Q. Shi, A. Dick, I. Reid. Joint Probabilistic Data Association Revisited. In ICCV, 2015.
CNNTCM
13. using public detections
27.6
29.6
±13.9
36.811.2% 44.0% 7,78634,733712 (16.4)943 (21.7)1.7Public
B. Wang, K. L. Chan, L. Wang, B. Shuai, Z. Zuo, T. Liu, G. Wang. Joint Learning of Convolutional Neural Networks and Temporally Constrained Metrics for Tracklet Association. In DeepVision Workshop (CVPR), 2016.
TSMLCDEnew
14. using public detections
22.8
34.3
±13.1
44.114.0% 39.4% 7,86931,908618 (12.9)959 (20.0)6.5Public
B. Wang, G. Wang, K. L. Chan, L. Wang. Tracklet Association by Online Target-Specific Metric Learning and Coherent Dynamics Estimation. In arXiv:1511.06654, 2015.
JointMC
15. using public detections
19.7
35.6
±18.9
45.123.2% 39.3% 10,58028,508457 (8.5)969 (18.1)0.6Public
M. Keuper, S. Tang, Z. Yu, B. Andres, T. Brox, B. Schiele. A Multi-cut Formulation for Joint Segmentation and Tracking of Multiple Objects. In CoRR, 2016.
MCF_PHD
16. using public detections
26.4
29.9
±20.0
38.211.9% 44.0% 8,89233,529656 (14.4)989 (21.8)12.2Public
N. Wojke, D. Paulus. Global data association for the Probability Hypothesis Density filter using network flows. In 2016 IEEE International Conference on Robotics and Automation, ICRA, 2016.
GSCR
17. online method using public detections
40.8
15.8
±10.5
27.91.8% 61.0% 7,59743,633514 (17.7)1,010 (34.8)28.1Public
L. Fagot-Bouquet, R. Audigier, Y. Dhome, F. Lerasle. Online multi-person tracking based on global sparse collaborative representations. In ICIP, 2015.
CEM
18. using public detections
41.3
19.3
±17.5
0.08.5% 46.5% 14,18034,591813 (18.6)1,023 (23.4)1.1Public
A. Milan, S. Roth, K. Schindler. Continuous Energy Minimization for Multitarget Tracking. In IEEE TPAMI, 2014.
MOTBKCF
19. online method using public detections
24.9
32.4
±15.3
44.614.1% 42.0% 8,91232,112501 (10.5)1,058 (22.2)0.2Public
Anonymous submission
MotiCon
20. using public detections
45.9
23.1
±16.4
29.44.7% 52.0% 10,40435,8441,018 (24.4)1,061 (25.5)1.4Public
L. Leal-Taixé, M. Fenzi, A. Kuznetsova, B. Rosenhahn, S. Savarese. Learning an image-based motion context for multiple people tracking. In CVPR, 2014.
TrackerAvg RankMOTAIDF1MTMLFPFNID Sw. FragHzDetector
LFNF
21. using public detections
29.3
31.6
±12.3
33.19.6% 41.7% 5,94335,095961 (22.4)1,106 (25.8)4.0Public
Anonymous submission
mLK
22. online method using public detections
19.4
35.1
±12.9
47.512.3% 38.3% 5,67833,815383 (8.5)1,175 (26.1)1.0Public
Yuan Zhang, Di Xie and Shiliang Pu (Hikvision Research Institute)
RSCNN
23. using public detections
30.0
29.5
±23.9
37.012.9% 36.3% 11,86630,474976 (19.4)1,176 (23.3)4.0Public
Heba Mahgoub, Khaled Mostafa, Khaled T. Wassif and Ibrahim Farag, “Multi-Target Tracking Using Hierarchical Convolutional Features and Motion Cues” International Journal of Advanced Computer Science and Applications(IJACSA), 8(11), 2017.
SCEA
24. online method using public detections
32.0
29.1
±12.2
37.28.9% 47.3% 6,06036,912604 (15.1)1,182 (29.6)6.8Public
J. Yoon, C. Lee, M. Yang, K. Yoon. Online Multi-object Tracking via Structural Constraint Event Aggregation. In IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), 2016.
OLTT
25. using public detections
46.2
21.0
±12.7
24.63.3% 54.4% 8,37639,368794 (22.1)1,199 (33.4)10.9Public
Anonymous submission
JAM
26. online method using public detections
27.1
32.0
±14.6
39.210.3% 43.6% 5,71435,473562 (13.3)1,217 (28.8)0.0Public
Anonymous submission
MTT
27. online method using public detections
40.3
25.0
±13.2
33.24.0% 53.0% 7,69137,833569 (14.8)1,218 (31.7)1.9Public
Anonymous submission
SSM_DPM
28. online method using public detections
35.9
15.1
±20.5
37.211.4% 44.5% 18,31933,193621 (13.5)1,229 (26.7)28.9Public
Anonymous submission
GMC
29. online method using public detections
27.9
25.0
±13.8
38.810.5% 44.4% 12,04633,441599 (13.1)1,246 (27.3)28.9Public
Anonymous submission
OMT_DFH
30. online method using public detections
35.0
21.2
±17.2
37.37.1% 46.5% 13,21834,657563 (12.9)1,255 (28.8)28.6Public
J. Ju, D. Kim, B. Ku, D. Han, H. Ko. Online multi-object tracking with efficient track drift and fragmentation handling. In J. Opt. Soc. Am. A, 2017.
TrackerAvg RankMOTAIDF1MTMLFPFNID Sw. FragHzDetector
APRCNN_Pub
31. online method using public detections
16.6
38.5
±9.9
47.18.7% 37.4% 4,00533,203586 (12.8)1,263 (27.5)6.7Public
C. Long, A. Haizhou, S. Chong, Z. Zijie, B. Bo. Online Multi-Object Tracking with Convolutional Neural Networks. In 2017 IEEE International Conference on Image Processing (ICIP), 2017.
GMPHD
32. online method using public detections
40.8
18.5
±12.7
28.43.9% 55.3% 7,86441,766459 (14.3)1,266 (39.5)19.8Public
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.
HybridDAT
33. online method using public detections
19.0
35.0
±15.0
47.711.4% 42.2% 8,45531,140358 (7.3)1,267 (25.7)4.6Public
M. Yang, Y. Jia. A Hybrid Data Association Framework for Robust Online Multi-Object Tracking. In IEEE Transactions on Image Processing, 2016.
2D_SPL
34. online method using public detections
37.3
25.2
±14.6
35.85.1% 50.3% 8,03737,190706 (17.9)1,278 (32.4)0.8Public
Anonymous submission
RMOT
35. online method using public detections
45.8
18.6
±17.5
32.65.3% 53.3% 12,47336,835684 (17.1)1,282 (32.0)7.9Public
J. Yoon, H. Yang, J. Lim, K. Yoon. Bayesian Multi-Object Tracking Using Motion Context from Multiple Objects. In IEEE Winter Conference on Applications of Computer Vision (WACV), 2015.
AR_WLS
36. using public detections
35.7
24.9
±14.5
35.46.1% 52.4% 8,07137,543551 (14.2)1,297 (33.3)2.1Public
Anonymous submission
AdTobKF
37. online method using public detections
33.1
24.8
±12.1
34.54.0% 52.0% 6,20139,321666 (18.5)1,300 (36.1)206.5Public
Anonymous submission
TENSOR
38. using public detections
43.5
24.3
±13.2
24.15.5% 46.6% 6,64438,5821,271 (34.2)1,304 (35.1)24.0Public
Anonymous submission
cuMOT
39. online method using public detections
49.0
6.8
±13.7
22.41.1% 59.4% 12,29844,142811 (28.8)1,306 (46.4)28.9Public
Anonymous submission
SiameseCNN
40. using public detections
32.9
29.0
±15.1
34.38.5% 48.4% 5,16037,798639 (16.6)1,316 (34.2)52.8Public
Laura Leal-Taixé, Cristian Canton-Ferrer, Konrad Schindler. Learning by Tracking: Siamese CNN for Robust Target Association. DeepVision Workshop (CVPR), Las Vegas (Nevada, USA), June 2016.
TrackerAvg RankMOTAIDF1MTMLFPFNID Sw. FragHzDetector
A_TKF
41. online method using public detections
33.3
24.0
±13.3
35.56.0% 50.5% 7,83938,174689 (18.2)1,365 (36.0)180.7Public
Anonymous submission
QuadMOT
42. using public detections
25.9
33.8
±14.8
40.412.9% 36.9% 7,89832,061703 (14.7)1,430 (29.9)3.7Public
J. Son, M. Baek, M. Cho, B. Han. Multi-Object Tracking with Quadruplet Convolutional Neural Networks. In CVPR, 2017.
AM
43. online method using public detections
20.1
34.3
±13.7
48.311.4% 43.4% 5,15434,848348 (8.0)1,463 (33.8)0.5Public
Q. Chu, W. Ouyang, H. Li, X. Wang, B. Liu, N. Yu. Online Multi-Object Tracking Using CNN-based Single Object Tracker with Spatial-Temporal Attention Mechanism. In arXiv preprint arXiv:1708.02843, 2017.
cf_mdp
44. online method using public detections
23.8
31.4
±14.7
43.716.1% 34.3% 10,11831,284718 (14.6)1,469 (29.9)1.4Public
Anonymous submission
Q_cf
45. online method using public detections new
23.1
31.4
±13.1
43.716.1% 34.3% 10,11831,284718 (14.6)1,469 (29.9)450.4Public
Anonymous submission
TBD_DL
46. online method using public detections
45.8
11.2
±18.5
32.45.3% 50.2% 17,17136,759651 (16.2)1,480 (36.8)170.1Public
Anonymous submission
EAMTTpub
47. online method using public detections
40.8
22.3
±14.2
32.85.4% 52.7% 7,92438,982833 (22.8)1,485 (40.6)12.2Public
R. Sanchez-Matilla, F. Poiesi, A. Cavallaro "Multi-target tracking with strong and weak detections" in BMTT ECCVw 2016
MDP
48. online method using public detections
28.8
30.3
±14.6
44.713.0% 38.4% 9,71732,422680 (14.4)1,500 (31.8)1.1Public
Y. Xiang, A. Alahi, S. Savarese. Learning to Track: Online Multi-Object Tracking by Decision Making. In International Conference on Computer Vision (ICCV), 2015.
TDAM
49. online method using public detections
25.3
33.0
±9.8
46.113.3% 39.1% 10,06430,617464 (9.2)1,506 (30.0)5.9Public
M. Yang, Y. Jia. Temporal dynamic appearance modeling for online multi-person tracking. In Computer Vision and Image Understanding, 2016.
TBSS15
50. online method using public detections
33.7
29.2
±12.5
37.26.8% 43.8% 6,06836,779649 (16.2)1,508 (37.6)11.5Public
Anonymous submission
TrackerAvg RankMOTAIDF1MTMLFPFNID Sw. FragHzDetector
STKSVD
51. online method using public detections
44.3
11.8
±18.5
33.25.1% 50.1% 17,07236,499641 (15.8)1,521 (37.5)1,156.6Public
Anonymous submission
RAR15pub
52. online method using public detections
22.8
35.1
±12.5
45.413.0% 42.3% 6,77132,717381 (8.1)1,523 (32.6)5.4Public
Anonymous ICCV submission
TBX
53. using public detections
41.8
27.5
±13.3
33.810.4% 45.8% 7,96835,810759 (18.2)1,528 (36.6)0.1Public
R. Henschel, L. Leal-Taixé, B. Rosenhahn, K. Schindler. Tracking with multi-level features. In arXiv:1607.07304, 2016.
TSDA_OAL
54. online method using public detections
40.8
18.6
±17.6
36.19.4% 42.3% 16,35032,853806 (17.3)1,544 (33.2)19.7Public
H. Ko. Online multi-person tracking with two-stage data association and online appearance model learning. In IET Computer Vision, 2017.
DCCRF
55. online method using public detections
28.5
33.6
±11.0
39.110.4% 37.6% 5,91734,002866 (19.4)1,566 (35.1)0.1Public
Anonymous submission
CDA_DDALpb
56. online method using public detections
25.8
32.8
±10.6
38.89.7% 42.2% 4,98335,690614 (14.7)1,583 (37.8)2.3Public
S. Bae and K. Yoon, Confidence-Based Data Association and Discriminative Deep Appearance Learning for Robust Online Multi-Object Tracking, In IEEE TPAMI, 2017.
AMT
57. online method using public detections new
20.8
30.4
±13.6
43.915.0% 35.6% 10,39131,641725 (14.9)1,641 (33.8)296.0Public
Anonymous submission
oICF
58. online method using public detections
35.4
27.1
±14.9
40.56.4% 48.7% 7,59436,757454 (11.3)1,660 (41.3)1.4Public
H. Kieritz, S. Becker, W. Hübner, M. Arens. Online Multi-Person Tracking using Integral Channel Features. In IEEE Advanced Video and Signal-based Surveillance (AVSS) 2016, 2016.
CF_MCMC
59. using public detections
30.4
31.4
±11.3
36.410.3% 40.9% 8,79832,541814 (17.3)1,711 (36.4)3.2Public
Anonymous submission
LP2D
60. using public detections
42.6
19.8
±14.2
0.06.7% 41.2% 11,58036,0451,649 (39.9)1,712 (41.4)112.1Public
MOT baseline: Linear programming on 2D image coordinates.
TrackerAvg RankMOTAIDF1MTMLFPFNID Sw. FragHzDetector
TC_ODAL
61. online method using public detections
56.2
15.1
±15.0
0.03.2% 55.8% 12,97038,538637 (17.1)1,716 (46.0)1.7Public
S. Bae, K. Yoon. Robust Online Multi-Object Tracking based on Tracklet Confidence and Online Discriminative Appearance Learning. In CVPR, 2014.
ELP
62. using public detections
39.4
25.0
±10.8
26.27.5% 43.8% 7,34537,3441,396 (35.6)1,804 (46.0)5.7Public
N. McLaughlin, J. Martinez Del Rincon, P. Miller. Enhancing Linear Programming with Motion Modeling for Multi-target Tracking. In IEEE Winter Conference on Applications of Computer Vision (WACV), 2015.
ALExTRAC
63. using public detections
50.3
17.0
±12.1
17.33.9% 52.4% 9,23339,9331,859 (53.1)1,872 (53.5)3.7Public
A. Bewley, L. Ott, F. Ramos, B. Upcroft. ALExTRAC: Affinity Learning by Exploring Temporal Reinforcement within Association Chains. In International Conference on Robotics and Automation (ICRA), (to appear) 2016.
RMM
64. using public detections
42.3
26.6
±12.3
31.25.1% 44.8% 6,70837,2811,088 (27.7)1,957 (49.8)9.4Public
Anonymous submission
TBD
65. using public detections
54.8
15.9
±17.6
0.06.4% 47.9% 14,94334,7771,939 (44.7)1,963 (45.2)0.7Public
A. Geiger, M. Lauer, C. Wojek, C. Stiller, R. Urtasun. 3D Traffic Scene Understanding from Movable Platforms. In Pattern Analysis and Machine Intelligence (PAMI), 2014.
CppSORT
66. online method using public detections
42.9
21.7
±11.8
26.83.7% 49.1% 8,42238,4541,231 (32.9)2,005 (53.6)1,112.1Public
S. Murray. Real-Time Multiple Object Tracking - A Study on the Importance of Speed. In arXiv preprint arXiv:1709.03572, 2017.
MPM
67. online method using public detections
46.3
24.8
±10.9
25.42.9% 50.2% 4,06140,8461,319 (39.4)2,017 (60.2)8.6Public
Anonymous submission
AMIR15
68. online method using public detections
21.2
37.6
±12.5
46.015.8% 26.8% 7,93329,3971,026 (19.7)2,024 (38.8)1.9Public
A. Sadeghian, A. Alahi, S. Savarese. Tracking The Untrackable: Learning To Track Multiple Cues with Long-Term Dependencies. In ICCV, 2017.
RNN_LSTM
69. online method using public detections
47.6
19.0
±15.2
17.15.5% 45.6% 11,57836,7061,490 (37.0)2,081 (51.7)165.2Public
A. Milan, S. Rezatofighi, A. Dick, I. Reid, K. Schindler. Online Multi-Target Tracking using Recurrent Neural Networks. In AAAI, 2017.
MVM
70. online method using public detections
44.9
25.0
±11.3
26.53.2% 48.7% 4,66640,1181,302 (37.5)2,084 (60.1)13.8Public
Anonymous submission
TrackerAvg RankMOTAIDF1MTMLFPFNID Sw. FragHzDetector
SMOT
71. using public detections
57.1
18.2
±10.3
0.02.8% 54.8% 8,78040,3101,148 (33.4)2,132 (62.0)2.7Public
C. Dicle, O. Camps, M. Sznaier. The Way They Move: Tracking Targets with Similar Appearance. In ICCV, 2013.
PHD_GSDL
72. online method using public detections
33.6
30.5
±14.9
38.87.6% 41.2% 6,53435,284879 (20.6)2,208 (51.9)8.2Public
Anonymous submission
MTSTracker
73. online method using public detections
40.5
20.6
±18.2
31.99.0% 36.9% 15,16132,2121,387 (29.2)2,357 (49.5)19.3Public
F. Nguyen Thi Lan Anh, F. Bremond. Multi-Object Tracking using Multi-Channel Part Appearance Representation. In International conference on Advanced video and Signal Based Surveillance, 2017.
Otakudj
74. online method using public detections
35.3
29.5
±14.2
43.011.8% 34.3% 9,43332,0671,798 (37.6)2,431 (50.8)0.6Public
Anonymous submission
MAR
75. using public detections
33.7
29.6
±11.6
42.811.7% 34.3% 9,36432,1311,783 (37.4)2,439 (51.1)1.2Public
Anonymous submission
RAM
76. using public detections
41.2
18.8
±12.4
31.112.6% 33.8% 14,38431,6953,814 (78.8)2,458 (50.8)8.2Public
Anonymous submission
HSA
77. online method using public detections
43.8
25.0
±14.4
29.75.0% 43.8% 7,64536,9361,504 (37.7)2,550 (63.9)2.7Public
Anonymous submission
MCFCOS_CNN
78. online method using public detections
41.3
25.5
±20.8
32.09.3% 34.4% 12,34431,3782,064 (42.2)2,618 (53.5)0.5Public
Anonymous submission
ARM
79. using public detections
30.4
29.0
±12.8
40.610.0% 36.3% 7,86633,8211,948 (43.3)2,711 (60.3)9.6Public
Anonymous submission
Dr_lzy
80. using public detections
38.3
-15.8
±21.6
24.116.6% 28.7% 38,04528,0295,079 (93.4)2,719 (50.0)9.6Public
Anonymous submission
TrackerAvg RankMOTAIDF1MTMLFPFNID Sw. FragHzDetector
Stitiching
81. using public detections
43.0
-4.6
±24.9
31.116.1% 28.8% 31,83528,9393,512 (66.4)2,840 (53.7)4.8Public
Anonymous submission
LDCT
82. online method using public detections
41.8
4.7
±41.3
16.811.4% 32.5% 14,06632,15612,348 (259.1)2,918 (61.2)20.7Public
F. Solera, S. Calderara, R. Cucchiara. Learning to Divide and Conquer for Online Multi-Target Tracking. In ICCV, 2015
DP_NMS
83. using public detections
43.8
14.5
±14.5
19.76.0% 40.8% 13,17134,8144,537 (104.7)3,090 (71.3)444.8Public
H. Pirsiavash, D. Ramanan, C. Fowlkes. Globally-Optimal Greedy Algorithms for Tracking a Variable Number of Objects. In CVPR, 2011.

Benchmark Statistics

SequencesFramesTrajectoriesBoxes
11578372161440

Difficulty Analysis

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

TUD-Crossing

TUD-Crossing

(61.0% MOTA)

PETS09-S2L2

PETS09-S2L2

(40.6% MOTA)

ETH-Jelmoli

ETH-Jelmoli

(37.1% MOTA)

...

...

Venice-1

Venice-1

(18.9% MOTA)

ADL-Rundle-1

ADL-Rundle-1

(14.4% 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.