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 Rank MOTAIDF1MTMLFPFNID Sw.FragHzDetector
NEW_NW
1. online method using public detections
49.4
-38.8
±53.1
2.00.1% 98.3% 25,60659,56980 (26.3)112 (36.8)5.8Public
Anonymous submission
JPDA_OP
2. online method using public detections new
37.8
3.6
±11.3
7.50.4% 96.1% 1,02458,18929 (5.5)119 (22.5)77.7Public
Anonymous submission
LDCT
3. online method using public detections
40.8
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
4. using public detections
44.3
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.
EFMC
5. online method using public detections
48.5
14.9
±9.8
11.53.3% 50.5% 5,58341,0855,623 (169.7)3,443 (103.9)24.6Public
Anonymous submission
TC_ODAL
6. online method using public detections
56.1
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.
GSCR
7. online method using public detections
41.3
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.
TBD
8. using public detections
55.3
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.
ALExTRAC
9. 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.
Bar_dist
10. online method using public detections
41.0
18.0
±16.0
17.65.7% 35.9% 10,01936,2224,161 (101.4)5,043 (122.9)2,891.5Public
Anonymous submission
TrackerAvg Rank MOTAIDF1MTMLFPFNID Sw.FragHzDetector
SMOT
11. using public detections
56.7
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.
KCF_Simple
12. online method using public detections
49.4
18.3
±11.1
25.12.6% 49.8% 8,97639,8051,436 (40.8)2,634 (74.8)35.6Public
Anonymous submission
GMPHD
13. 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.
TSDA_OAL
14. online method using public detections
40.7
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.
RMOT
15. online method using public detections
46.4
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.
RNN_LSTM
16. online method using public detections
47.8
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.
CEM
17. using public detections
41.5
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.
DCO_X
18. using public detections
42.9
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.
LP2D
19. using public detections
43.9
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.
Bar_dist2
20. online method using public detections
38.9
20.1
±14.5
21.77.4% 36.8% 11,16134,8743,084 (71.3)3,823 (88.4)215.8Public
Anonymous submission
TrackerAvg Rank MOTAIDF1MTMLFPFNID Sw.FragHzDetector
TC_SIAMESE
21. online method using public detections
40.8
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.
MTSTracker
22. online method using public detections
39.6
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
23. online method using public detections
39.9
20.8
±14.5
37.49.2% 38.6% 15,50131,9881,172 (24.4)2,325 (48.5)7.2Public
Anonymous submission
OMT_DFH
24. online method using public detections
35.9
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.
CppSORT
25. online method using public detections
42.5
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.
EDA_GNN
26. online method using public detections
37.8
21.8
±13.8
27.89.0% 40.2% 11,97034,5871,488 (34.0)1,851 (42.4)56.4Public
Paper ID 2713
SAS_MOT15
27. using public detections
44.7
22.2
±13.8
27.23.1% 61.6% 5,59141,531700 (21.6)1,240 (38.3)8.9Public
Submission id 177
EAMTTpub
28. 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
DCOR
29. online method using public detections new
40.4
22.4
±12.1
24.73.3% 57.4% 5,60341,410634 (19.4)1,686 (51.7)28.9Public
Anonymous submission
SegTrack
30. using public detections
44.8
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.
TrackerAvg Rank MOTAIDF1MTMLFPFNID Sw.FragHzDetector
ZRT
31. online method using public detections
41.0
22.8
±19.3
37.89.7% 38.4% 14,42331,9321,080 (22.5)2,267 (47.2)5.3Public
Anonymous submission
MotiCon
32. using public detections
45.8
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.
YT
33. using public detections
45.7
23.5
±11.6
26.84.4% 49.5% 5,53740,2101,270 (36.8)1,817 (52.6)34.4Public
Anonymous submission
JPDA_m
34. 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.
TFMOT
35. online method using public detections
38.4
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.
siam
36. online method using public detections
47.2
23.9
±17.1
23.35.7% 37.7% 8,69535,1302,939 (68.6)3,886 (90.7)1.9Public
Anonymous submission
LINF1
37. using public detections
33.8
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.
ELP
38. using public detections
40.6
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.
HAM_SADF
39. online method using public detections
30.8
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.
LP_SSVM
40. using public detections
34.7
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 Rank MOTAIDF1MTMLFPFNID Sw.FragHzDetector
MCFCOS_CNN
41. online method using public detections
40.8
25.5
±20.8
32.09.3% 34.4% 12,34431,3782,064 (42.2)2,618 (53.5)0.5Public
Anonymous submission
TO
42. using public detections
37.9
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.
DAC_min
43. online method using public detections
31.8
26.1
±13.3
36.96.8% 50.1% 8,15036,736519 (12.9)1,046 (26.0)11.6Public
GIST
DAC_min
44. online method using public detections
30.5
26.1
±13.6
37.58.9% 48.1% 9,47835,382539 (12.7)1,115 (26.3)11.6Public
oICF
45. online method using public detections
36.8
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.
TBX
46. using public detections
41.3
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.
SLTV15
47. online method using public detections
29.9
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
CMOT
48. online method using public detections
28.0
27.6
±14.0
43.310.5% 49.0% 9,45134,207800 (18.0)1,584 (35.7)5,783.0Public
Anonymous submission
DS_RNN
49. online method using public detections
35.8
27.8
±11.4
29.67.1% 37.9% 6,90335,2382,192 (51.4)3,011 (70.6)19.3Public
Anonymous submission
HAM_INTP15
50. online method using public detections
25.3
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.
TrackerAvg Rank MOTAIDF1MTMLFPFNID Sw.FragHzDetector
SiameseCNN
51. using public detections
32.8
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.
SCEA
52. online method using public detections
33.2
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
53. online method using public detections
35.2
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.
DSA_MOT
54. online method using public detections
26.7
29.4
±13.0
38.210.0% 45.1% 7,69135,146545 (12.7)1,133 (26.5)9.6Public
Anonymous submission
RSCNN
55. using public detections
30.5
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.
CNNTCM
56. using public detections
28.3
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.
MCF_PHD
57. using public detections
27.3
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.
MDP
58. online method using public detections
28.9
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.
PHD_GSDL
59. online method using public detections
34.1
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.
SNM
60. online method using public detections
30.8
31.3
±16.5
38.212.6% 35.4% 8,90332,393926 (19.6)2,382 (50.4)14.8Public
Anonymous submission
TrackerAvg Rank MOTAIDF1MTMLFPFNID Sw.FragHzDetector
CF_MCMC
61. using public detections
30.8
31.4
±11.3
36.410.3% 40.9% 8,79832,541814 (17.3)1,711 (36.4)3.2Public
Anonymous submission
cf_mdp
62. online method using public detections
18.7
31.4
±14.7
43.716.1% 34.3% 10,11831,284718 (14.6)1,469 (29.9)450.4Public
Anonymous submission
LFNF
63. using public detections
30.5
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
MHT_DAM
64. using public detections
24.3
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.
CDA_DDALpb
65. online method using public detections
26.3
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.
TDAM
66. online method using public detections
26.2
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.
DCCRF
67. online method using public detections
27.8
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.
NOMT
68. using public detections
21.0
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.
QuadMOT
69. 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.
TSMLCDEnew
70. using public detections
23.6
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.
TrackerAvg Rank MOTAIDF1MTMLFPFNID Sw.FragHzDetector
AM
71. online method using public detections
20.6
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.
HybridDAT
72. online method using public detections
21.1
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.
mLK
73. online method using public detections
21.0
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)
RAR15pub
74. online method using public detections
23.2
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.
TSN
75. using public detections
25.2
35.5
±12.1
43.014.4% 43.6% 5,68233,515454 (10.0)967 (21.3)0.8Public
Anonymous submission
JointMC
76. using public detections
21.5
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.
TripT15
77. online method using public detections
29.7
35.7
±14.9
47.711.1% 39.8% 8,72930,152655 (12.9)1,614 (31.7)1.1Public
Anonymous submission
SST_MOT15
78. online method using public detections
29.3
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
TripBFT15
79. online method using public detections
25.2
37.1
±15.3
48.412.6% 39.7% 8,30529,732580 (11.2)1,193 (23.1)1.0Public
Anonymous submission
AMIR15
80. online method using public detections
21.5
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.
TrackerAvg Rank MOTAIDF1MTMLFPFNID Sw.FragHzDetector
AP_HWDPL_p
81. online method using public detections
16.8
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.
CRF_RNN15
82. using public detections
18.8
38.9
±15.4
49.320.9% 29.4% 10,66926,291591 (10.3)1,270 (22.2)1.3Public
Anonymous submission
BnW
83. online method using public detections
18.8
42.9
±14.0
47.925.4% 25.2% 8,33825,813926 (16.0)1,652 (28.5)4.0Public
Anonymous submission

Benchmark Statistics

SequencesFramesTrajectoriesBoxes
11578372161440

Difficulty Analysis

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

TUD-Crossing

TUD-Crossing

(60.0% MOTA)

PETS09-S2L2

PETS09-S2L2

(40.1% MOTA)

ETH-Jelmoli

ETH-Jelmoli

(36.6% MOTA)

...

...

Venice-1

Venice-1

(20.2% MOTA)

ADL-Rundle-1

ADL-Rundle-1

(15.6% 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.