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 RankMOTAMOTPFAFMTMLFPFNID Sw.FragHzDetector
APRCNN_Pub
1. online method using public detections
15.5
38.5
±9.9
72.60.78.7% 37.4% 4,00533,203586 (12.8)1,263 (27.5)6.7Public
Anonymous submission
MDPNN
2. online method using public detections
24.4
37.6
±12.5
71.71.415.8% 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 arXiv preprint arXiv:1701.01909, 2017.
JointMC
3. using public detections
21.9
35.6
±18.9
71.91.823.2% 39.3% 10,58028,508457 (8.5)969 (18.1)0.6Public
RAR15pub
4. online method using public detections
22.4
35.1
±12.5
70.91.213.0% 42.3% 6,77132,717381 (8.1)1,523 (32.6)5.4Public
Anonymous ICCV submission
mLK
5. online method using public detections
17.6
35.1
±12.9
71.51.012.3% 38.3% 5,67833,815383 (8.5)1,175 (26.1)1.0Public
Yuan Zhang, Di Xie and Shiliang Pu (Hikvision Research Institute)
TSMLCDEnew
6. using public detections
18.8
34.3
±13.1
71.71.414.0% 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.
QuadMOT
7. using public detections
21.5
33.8
±14.8
73.41.412.9% 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.
NOMT
8. using public detections
17.0
33.7
±16.2
71.91.312.2% 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.
TDAM
9. online method using public detections
22.4
33.0
±9.8
72.81.713.3% 39.1% 10,06430,617464 (9.2)1,506 (30.0)5.9Public
Y. Min, J. Yunde. Temporal Dynamic Appearance Modeling for Online Multi-Person Tracking. In arXiv:1510.02906, 2015.
HAF
10. using public detections
22.8
33.0
±17.8
71.71.716.4% 44.9% 9,59331,204376 (7.6)804 (16.3)0.6Public
Anonymous submission
TrackerAvg RankMOTAMOTPFAFMTMLFPFNID Sw.FragHzDetector
CDA_DDALpb
11. online method using public detections
27.5
32.8
±10.6
70.70.99.7% 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.
omdpsplit
12. using public detections
26.8
32.6
±14.3
71.51.716.0% 34.4% 10,10530,704580 (11.6)1,268 (25.3)0.1Public
Anonymous submission
HG
13. online method using public detections
34.2
32.4
±12.2
70.31.18.9% 34.5% 6,48934,176882 (19.9)2,496 (56.2)0.5Public
CVPR #469
MHT_DAM
14. using public detections
22.8
32.4
±15.6
71.81.616.0% 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.
JAM
15. online method using public detections
26.7
32.0
±14.6
71.21.010.3% 43.6% 5,71435,473562 (13.3)1,217 (28.8)0.0Public
Anonymous submission
CCF_MOT
16. online method using public detections
23.6
31.6
±14.2
71.81.010.1% 46.3% 6,06935,444491 (11.6)994 (23.5)0.9Public
icip #2242 Online Multiple Object Tracking via Flow and Convolutional Features
CF_MCMC
17. using public detections
34.9
31.4
±11.3
69.81.510.3% 40.9% 8,79832,541814 (17.3)1,711 (36.4)3.2Public
Anonymous submission
OAB
18. online method using public detections
27.7
31.2
±14.8
70.81.09.8% 46.3% 5,91635,974391 (9.4)1,115 (26.9)0.8Public
Anonymous submission
MPTCNN
19. online method using public detections
32.1
30.9
±12.9
70.11.39.4% 43.7% 7,37434,623484 (11.1)1,582 (36.2)0.6Public
Anonymous submission
TSSRC_2015
20. online method using public detections
28.9
30.5
±17.1
71.01.814.6% 41.1% 10,65031,445612 (12.5)1,585 (32.5)34.3Public
Anonymous submission
TrackerAvg RankMOTAMOTPFAFMTMLFPFNID Sw.FragHzDetector
MDP
21. online method using public detections
30.7
30.3
±14.6
71.31.713.0% 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.
OMDP15
22. using public detections
33.9
30.3
±14.4
71.11.915.3% 34.1% 11,09331,057682 (13.8)1,635 (33.1)0.1Public
Anonymous submission
EAGS
23. using public detections
25.3
30.0
±15.0
71.41.715.8% 37.7% 9,99431,8941,149 (23.9)1,231 (25.6)192.8Public
#MM-007925 Enhancing Association Graph with Super-voxel for Multi-target Tracking
DSR
24. using public detections
21.7
29.8
±13.1
72.21.09.7% 54.5% 5,69237,153269 (6.8)688 (17.4)2.6Public
Anonymous submission
CNNTCM
25. using public detections
26.3
29.6
±13.9
71.81.311.2% 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.
SCEA
26. online method using public detections
28.2
29.1
±12.2
71.11.08.9% 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.
SiameseCNN
27. using public detections
27.3
29.0
±15.1
71.20.98.5% 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.
TBX
28. using public detections
38.9
27.5
±13.3
70.61.410.4% 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.
oICF
29. online method using public detections
36.5
27.1
±14.9
70.01.36.4% 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.
TO
30. using public detections
26.9
25.7
±13.5
72.20.84.3% 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.
TrackerAvg RankMOTAMOTPFAFMTMLFPFNID Sw.FragHzDetector
2D_SPL
31. using public detections
39.5
25.2
±14.6
71.21.45.1% 50.3% 8,03737,190706 (17.9)1,278 (32.4)0.8Public
Anonymous submission
LP_SSVM
32. using public detections
30.7
25.2
±13.7
71.71.45.8% 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.
ELP
33. using public detections
36.1
25.0
±10.8
71.21.37.5% 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.
AR_WLS
34. using public detections
37.5
24.9
±14.5
71.01.46.1% 52.4% 8,07137,543551 (14.2)1,297 (33.3)2.1Public
Anonymous submission
MPM
35. online method using public detections
37.5
24.8
±10.9
71.20.72.9% 50.2% 4,06140,8461,319 (39.4)2,017 (60.2)8.6Public
Anonymous submission
LINF1
36. using public detections
29.2
24.5
±15.4
71.31.05.5% 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.
MPO
37. online method using public detections
40.2
24.3
±11.1
70.90.83.1% 48.7% 4,72340,3401,459 (42.5)2,209 (64.3)11.3Public
Anonymous submission
TFMOT
38. online method using public detections
28.8
23.8
±11.9
71.30.84.9% 62.0% 4,53341,873404 (12.7)792 (24.9)11.3Public
Joint Cost Minimization for Multi-Object Tracking
JCM_MOT
39. online method using public detections
29.8
23.8
±12.0
71.30.84.9% 62.0% 4,53341,873404 (12.7)792 (24.9)11.3Public
Joint Cost Minimization for Multi-Object Tracking
JPDA_m
40. using public detections
34.0
23.8
±15.1
68.21.15.0% 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.
TrackerAvg RankMOTAMOTPFAFMTMLFPFNID Sw.FragHzDetector
MTTCNN
41. using public detections
37.1
23.5
±15.3
71.40.92.6% 54.0% 5,44840,802739 (22.0)1,208 (36.0)1.9Public
Anonymous submission
SFT_ACF
42. online method using public detections
43.2
23.2
±14.1
71.11.65.5% 43.0% 9,29536,5271,363 (33.6)2,363 (58.3)2.3Public
Anonymous submission
MotiCon
43. using public detections
44.4
23.1
±16.4
70.91.84.7% 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.
Otakudj
44. online method using public detections
37.6
23.0
±14.0
70.72.217.8% 19.6% 12,76729,9144,625 (90.1)3,524 (68.7)11.6Public
Anonymous submission
SegTrack
45. using public detections
38.7
22.5
±15.2
71.71.45.8% 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.
EAMTTpub
46. online method using public detections
40.9
22.3
±14.2
70.81.45.4% 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
OMT_DFH
47. online method using public detections
39.8
21.2
±17.2
69.92.37.1% 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.
MTT
48. online method using public detections
43.4
21.2
±13.0
71.21.43.3% 54.4% 8,27439,381790 (22.0)1,193 (33.2)1.9Public
Anonymous submission
OLTT
49. using public detections
41.9
21.0
±12.7
71.21.43.3% 54.4% 8,37639,368794 (22.1)1,199 (33.4)10.9Public
Anonymous submission
simRNN
50. using public detections
49.0
20.3
±12.0
71.01.65.0% 54.8% 9,40638,588966 (26.0)1,464 (39.4)0.4Public
Anonymous submission
TrackerAvg RankMOTAMOTPFAFMTMLFPFNID Sw.FragHzDetector
LP2D
51. using public detections
40.2
19.8
±14.2
71.22.06.7% 41.2% 11,58036,0451,649 (39.9)1,712 (41.4)112.1Public
MOT baseline: Linear programming on 2D image coordinates.
DCO_X
52. using public detections
42.9
19.6
±14.1
71.41.85.1% 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.
CEM
53. using public detections
44.3
19.3
±17.5
70.72.58.5% 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.
RNN_LSTM
54. online method using public detections
44.5
19.0
±15.2
71.02.05.5% 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.
RMOT
55. online method using public detections
46.9
18.6
±17.5
69.62.25.3% 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.
TSDA_OAL
56. online method using public detections
42.8
18.6
±17.6
69.72.89.4% 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.
GMPHD_15
57. online method using public detections
40.0
18.5
±12.7
70.91.43.9% 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.
SMOT
58. using public detections
49.6
18.2
±10.3
71.21.52.8% 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.
ALExTRAC
59. using public detections
48.8
17.0
±12.1
71.21.63.9% 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.
GMCSS
60. online method using public detections
52.6
16.7
±14.7
69.22.24.0% 55.1% 12,54338,102562 (14.8)1,619 (42.6)1.5Public
Anonymous submission
TrackerAvg RankMOTAMOTPFAFMTMLFPFNID Sw.FragHzDetector
TBD
61. using public detections
52.1
15.9
±17.6
70.92.66.4% 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.
GSCR
62. online method using public detections
42.3
15.8
±10.5
69.41.31.8% 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.
TC_ODAL
63. online method using public detections
54.3
15.1
±15.0
70.52.23.2% 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
64. using public detections
45.9
14.5
±13.9
70.82.36.0% 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.
FOT
65. online method using public detections
35.7
10.8
±17.0
71.42.115.4% 29.7% 12,05631,35711,373 (232.3)3,341 (68.2)96.4Public
Anonymous submission
MCFCOS_CNN
66. online method using public detections
50.7
10.1
±14.0
69.83.67.9% 37.3% 20,94131,6942,603 (53.8)2,870 (59.3)0.5Public
Anonymous submission
LDCT
67. online method using public detections
39.0
4.7
±41.3
71.72.411.4% 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
cuMOT
68. online method using public detections
28.9
0.8
±13.6
72.60.00.3% 98.3% 16160,72136 (30.8)62 (53.0)28.9Public
Anonymous submission

Benchmark Statistics

SequencesFramesTrajectoriesBoxes
11578372161440

Difficulty Analysis

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

TUD-Crossing

TUD-Crossing

(63.3% MOTA)

PETS09-S2L2

PETS09-S2L2

(42.1% MOTA)

ETH-Jelmoli

ETH-Jelmoli

(38.1% MOTA)

...

...

Venice-1

Venice-1

(23.2% MOTA)

ADL-Rundle-1

ADL-Rundle-1

(16.6% 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.
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 [2].
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] 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.