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 RankMOTAIDF1MTML FPFNID Sw.FragHzDetector
JPDA_OP
1. online method using public detections
40.6
3.6
±11.3
7.50.4% 96.1% 1,02458,18929 (5.5)119 (22.5)77.7Public
Anonymous submission
AP_HWDPL_p
2. online method using public detections
18.3
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.
SST_MOT15
3. online method using public detections
31.8
35.8
±19.3
39.67.8% 39.0% 4,06533,6691,728 (38.2)1,312 (29.0)6.3Public
Shijie Sun, Naveed Akhtar, Ajmal Mian
TFMOT
4. online method using public detections
41.3
23.8
±11.9
32.34.9% 62.0% 4,53341,873404 (12.7)792 (24.9)11.3Public
M. Abhijeet Boragule. Joint Cost Minimization for Multi-Object Tracking. In 2017 IEEE International Conference on Advanced Vide and Signale Based Surveillance, 2017.
TO
5. using public detections
40.8
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.
CDA_DDALpb
6. online method using public detections
28.1
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.
siam
7. online method using public detections
34.6
33.0
±17.0
36.28.9% 43.3% 5,10135,190853 (20.0)1,078 (25.2)1.9Public
Anonymous submission
AM
8. online method using public detections
22.2
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 2017 IEEE International Conference on Computer Vision (ICCV), 2017.
SiameseCNN
9. using public detections
35.3
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.
YT
10. using public detections
49.2
23.5
±11.6
26.84.4% 49.5% 5,53740,2101,270 (36.8)1,817 (52.6)34.4Public
Anonymous submission
TrackerAvg RankMOTAIDF1MTML FPFNID Sw.FragHzDetector
EFMC
11. online method using public detections
52.3
14.9
±9.8
11.53.3% 50.5% 5,58341,0855,623 (169.7)3,443 (103.9)24.6Public
Anonymous submission
SAS_MOT15
12. using public detections
48.3
22.2
±13.8
27.23.1% 61.6% 5,59141,531700 (21.6)1,240 (38.3)8.9Public
Submission id 177
DCOR
13. online method using public detections
43.3
22.4
±12.1
24.73.3% 57.4% 5,60341,410634 (19.4)1,686 (51.7)37.6Public
Anonymous submission
mLK
14. online method using public detections
22.8
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)
TSN
15. using public detections
27.0
35.5
±12.1
43.014.4% 43.6% 5,68233,515454 (10.0)967 (21.3)0.8Public
Anonymous submission
LINF1
16. using public detections
36.4
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.
DCCRF
17. online method using public detections
29.9
33.6
±11.0
39.110.4% 37.6% 5,91734,002866 (19.4)1,566 (35.1)0.1Public
H. Zhou, W. Ouyang, J. Cheng, X. Wang, H. Li. Deep Continuous Conditional Random Fields with Asymmetric Inter-object Constraints for Online Multi-object Tracking. In IEEE Transactions on Circuits and Systems for Video Technology, 2018.
LFNF
18. using public detections
32.8
31.6
±12.3
33.19.6% 41.7% 5,94335,095961 (22.4)1,106 (25.8)4.0Public
Sheng H, Hao L, Chen J, et al. Robust Local Effective Matching Model for Multi-Target Tracking. In PCM, 2017
SCEA
19. online method using public detections
35.7
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.
TBSS15
20. online method using public detections
37.7
29.2
±12.5
37.26.8% 43.8% 6,06836,779649 (16.2)1,508 (37.6)11.5Public
X. Zhou, P. Jiang, Z. Wei, H. Dong, F. Wang. Online Multi-Object Tracking with Structural Invariance Constraint. In BMVC, 2018.
TrackerAvg RankMOTAIDF1MTML FPFNID Sw.FragHzDetector
TC_SIAMESE
21. online method using public detections
43.9
20.2
±13.9
32.62.6% 67.5% 6,12742,596294 (9.6)825 (26.9)13.0Public
Y. Yoon, Y. Song, K. Yoon, M. Jeon. Online Multiple-Object Tracking using Selective Deep Appearance Matching. In IEEE/IEIE The International Conference on Consumer Electronics (ICCE) Asia, 2018.
DeepMP
22. using public detections
18.6
40.5
±12.8
28.816.8% 35.2% 6,27929,654599 (11.6)1,034 (20.0)9.6Public
Anonymous submission
JPDA_m
23. using public detections
35.8
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.
HSJ_Sia
24. online method using public detections
48.3
20.9
±13.0
29.24.0% 51.6% 6,45740,4771,695 (49.7)2,734 (80.1)70.3Public
Anonymous submission
PHD_GSDL
25. online method using public detections
36.9
30.5
±14.9
38.87.6% 41.2% 6,53435,284879 (20.6)2,208 (51.9)8.2Public
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.
SLTV15
26. online method using public detections
32.3
27.6
±15.1
40.37.2% 51.9% 6,58137,566358 (9.2)884 (22.7)20.9Public
Gwangju Institute of Science and Technology(GIST), Machine Learning and Vision Laboratory
TENSOR
27. using public detections
46.7
24.3
±13.2
24.15.5% 46.6% 6,64438,5821,271 (34.2)1,304 (35.1)24.0Public
X. Shi, H. Ling, Y. Pang, W. Hu, P. Chu, J. Xing. Rank-1 Tensor Approximation for High-Order Association in Multi-target Tracking. In IJCV, 2019.
RAR15pub
28. online method using public detections
25.3
35.1
±12.5
45.413.0% 42.3% 6,77132,717381 (8.1)1,523 (32.6)5.4Public
K. Fang, Y. Xiang, X. Li, S. Savarese. Recurrent Autoregressive Networks for Online Multi-Object Tracking. In The IEEE Winter Conference on Applications of Computer Vision (WACV), 2018.
DS_RNN
29. online method using public detections
38.8
27.8
±11.4
29.67.1% 37.9% 6,90335,2382,192 (51.4)3,011 (70.6)19.3Public
Anonymous submission
KCF
30. online method using public detections
23.4
38.9
±14.5
44.516.6% 31.5% 7,32129,501720 (13.9)1,440 (27.7)0.3Public
P. Chu, H. Fan, C. Tan, H. Ling. Online Multi-Object Tracking with Instance-Aware Tracker and Dynamic Model Refreshment. In WACV, 2019.
TrackerAvg RankMOTAIDF1MTML FPFNID Sw.FragHzDetector
HAM_SADF
31. online method using public detections
33.1
25.2
±13.9
37.85.7% 58.3% 7,33038,275357 (9.5)745 (19.8)18.7Public
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.
ELP
32. using public detections
43.8
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.
DEEPDA_MOT
33. online method using public detections
43.9
22.5
±17.7
25.96.4% 62.0% 7,34639,0921,159 (31.9)1,538 (42.3)172.8Public
K. Yoon, D. Kim, Y. Yoon, M. Jeon. Data Association for Multi-Object Tracking via Deep Neural Networks. In Sensors, 2019.
HAM_INTP15
34. online method using public detections
27.4
28.6
±13.8
41.410.0% 44.0% 7,48535,910460 (11.1)1,038 (25.0)18.7Public
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.
oICF
35. online method using public detections
39.8
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.
GSCR
36. online method using public detections
44.5
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.
DSA_MOT
37. online method using public detections
28.8
29.4
±13.0
38.210.0% 45.1% 7,69135,146545 (12.7)1,133 (26.5)9.6Public
Anonymous submission
NOMT
38. using public detections
23.1
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.
CNNTCM
39. using public detections
30.4
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.
GMPHD
40. online method using public detections
44.3
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.
TrackerAvg RankMOTAIDF1MTML FPFNID Sw.FragHzDetector
TSMLCDEnew
41. using public detections
25.9
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.
SegTrack
42. using public detections
48.3
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.
QuadMOT
43. using public detections
28.4
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.
EAMTTpub
44. online method using public detections
44.2
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
AMIR15
45. online method using public detections
23.3
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.
TBX
46. using public detections
44.5
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.
MHT__ReID
47. using public detections
26.7
32.6
±15.1
45.014.3% 45.5% 8,10132,907376 (8.1)766 (16.5)0.3Public
Anonymous submission
DAC_min
48. online method using public detections
34.2
26.1
±13.3
36.96.8% 50.1% 8,15036,736519 (12.9)1,046 (26.0)11.6Public
GIST
TripBFT15
49. online method using public detections
27.5
37.1
±15.3
48.412.6% 39.7% 8,30529,732580 (11.2)1,193 (23.1)1.0Public
Anonymous submission
LP_SSVM
50. using public detections
37.5
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 RankMOTAIDF1MTML FPFNID Sw.FragHzDetector
CppSORT
51. online method using public detections
45.6
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.
HybridDAT
52. online method using public detections
23.0
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.
TripT15
53. online method using public detections
32.1
35.7
±14.9
47.711.1% 39.8% 8,72930,152655 (12.9)1,614 (31.7)1.1Public
Anonymous submission
SMOT
54. using public detections
60.9
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.
CF_MCMC
55. using public detections
33.0
31.4
±11.3
36.410.3% 40.9% 8,79832,541814 (17.3)1,711 (36.4)3.2Public
Anonymous submission
MCF_PHD
56. using public detections
29.7
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.
SNM
57. online method using public detections
33.6
31.3
±16.5
38.212.6% 35.4% 8,90332,393926 (19.6)2,382 (50.4)14.8Public
Anonymous submission
KCF_Simple
58. online method using public detections
53.6
18.3
±11.1
25.12.6% 49.8% 8,97639,8051,436 (40.8)2,634 (74.8)35.6Public
Anonymous submission
MHT_DAM
59. using public detections
26.2
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.
ALExTRAC
60. using public detections
54.3
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.
TrackerAvg RankMOTAIDF1MTML FPFNID Sw.FragHzDetector
CMOT
61. online method using public detections
30.3
27.6
±14.0
43.310.5% 49.0% 9,45134,207800 (18.0)1,584 (35.7)5,783.0Public
Anonymous submission
DAC_min
62. online method using public detections
32.8
26.1
±13.6
37.58.9% 48.1% 9,47835,382539 (12.7)1,115 (26.3)11.6Public
MDP
63. online method using public detections
31.3
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.
Bar_dist
64. online method using public detections
44.3
18.0
±16.0
17.65.7% 35.9% 10,01936,2224,161 (101.4)5,043 (122.9)2,891.5Public
Anonymous submission
TDAM
65. online method using public detections
28.5
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.
cf_mdp
66. online method using public detections
20.3
31.4
±14.7
43.716.1% 34.3% 10,11831,284718 (14.6)1,469 (29.9)450.4Public
Anonymous submission
MotiCon
67. using public detections
49.2
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.
JointMC
68. using public detections
22.8
35.6
±18.9
45.123.2% 39.3% 10,58028,508457 (8.5)969 (18.1)0.6Public
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.
DCO_X
69. using public detections
45.8
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.
CRF_RNN15
70. using public detections
19.3
38.9
±15.4
49.320.9% 29.4% 10,66926,291591 (10.3)1,270 (22.2)3.2Public
Anonymous submission
TrackerAvg RankMOTAIDF1MTML FPFNID Sw.FragHzDetector
Bar_dist2
71. online method using public detections
42.2
20.1
±14.5
21.77.4% 36.8% 11,16134,8743,084 (71.3)3,823 (88.4)215.8Public
Anonymous submission
RNN_LSTM
72. online method using public detections
51.5
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.
LP2D
73. using public detections
47.3
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.
RSCNN
74. using public detections
33.2
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.
EDA_GNN
75. online method using public detections
40.8
21.8
±13.8
27.89.0% 40.2% 11,97034,5871,488 (34.0)1,851 (42.4)56.4Public
Paper ID 2713
MCFCOS_CNN
76. online method using public detections
44.0
25.5
±20.8
32.09.3% 34.4% 12,34431,3782,064 (42.2)2,618 (53.5)0.5Public
Anonymous submission
RMOT
77. online method using public detections
49.9
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.
TC_ODAL
78. online method using public detections
60.1
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.
DP_NMS
79. using public detections
47.5
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.
OMT_DFH
80. online method using public detections
38.6
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 RankMOTAIDF1MTML FPFNID Sw.FragHzDetector
LDCT
81. online method using public detections
44.4
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
CEM
82. using public detections
44.3
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.
ZRT
83. online method using public detections
43.9
22.8
±19.3
37.89.7% 38.4% 14,42331,9321,080 (22.5)2,267 (47.2)5.3Public
Anonymous submission
TBD
84. using public detections
59.3
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.
MTSTracker
85. online method using public detections
42.9
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.
RETR15
86. online method using public detections
43.0
20.8
±14.5
37.49.2% 38.6% 15,50131,9881,172 (24.4)2,325 (48.5)7.2Public
Anonymous submission
TSDA_OAL
87. online method using public detections
43.8
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.
HOHOTRACK
88. online method using public detections
33.3
26.8
±21.7
32.928.6% 16.9% 18,99424,5491,411 (23.5)3,417 (56.9)26.7Public
Anonymous submission
NEW_NW
89. online method using public detections
53.3
-38.8
±53.1
2.00.1% 98.3% 25,60659,56980 (26.3)112 (36.8)5.8Public
Anonymous submission

Benchmark Statistics

SequencesFramesTrajectoriesBoxes
11578372161440

Difficulty Analysis

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

TUD-Crossing

TUD-Crossing

(60.8% MOTA)

PETS09-S2L2

PETS09-S2L2

(40.5% MOTA)

ETH-Jelmoli

ETH-Jelmoli

(36.4% MOTA)

...

...

Venice-1

Venice-1

(20.3% MOTA)

ADL-Rundle-1

ADL-Rundle-1

(16.1% 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.