MOT16 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 RankMOTAIDF1MTMLFPFN ID Sw.FragHzDetector
NOMT
1. using public detections
16.4
46.4
±9.9
53.318.3% 41.4% 9,75387,565359 (6.9)504 (9.7)2.6Public
W. Choi. Near-Online Multi-target Tracking with Aggregated Local Flow Descriptor. In ICCV, 2015.
DP_NMS
2. using public detections
29.3
26.2
±9.3
31.24.1% 67.5% 3,689130,557365 (12.9)638 (22.5)5.9Public
H. Pirsiavash, D. Ramanan, C. Fowlkes. Globally-Optimal Greedy Algorithms for Tracking a Variable Number of Objects. In CVPR, 2011.
JPDA_m
3. using public detections
30.6
26.2
±6.1
0.04.1% 67.5% 3,689130,549365 (12.9)638 (22.5)22.2Public
H. Rezatofighi, A. Milan, Z. Zhang, Q. Shi, A. Dick, I. Reid. Joint Probabilistic Data Association Revisited. In ICCV, 2015.
MCjoint
4. using public detections
17.4
47.1
±10.8
52.320.4% 46.9% 6,70389,368370 (7.3)598 (11.7)0.6Public
}@article{DBLP:journals/corr/KeuperTYABS16, author = {Margret Keuper and Siyu Tang and Zhongjie Yu and Bjoern Andres and Thomas Brox and Bernt Schiele}, title = {A Multi-cut Formulation for Joint Segmentation and Tracking of Multiple Objects}, journal = {CoRR}, volume = {abs/1607.06317}, year = {2016}, url = {http://arxiv.org/abs/1607.06317}, timestamp = {Wed, 07 Jun 2017 14:41:31 +0200}, biburl = {http://dblp.uni-trier.de/rec/bib/journals/corr/KeuperTYABS16}, bibsource = {dblp computer science bibliography, http://dblp.org} }
oICF
5. online method using public detections
28.3
43.2
±10.2
49.311.3% 48.5% 6,65196,515381 (8.1)1,404 (29.8)0.4Public
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.
cm_test
6. online method using public detections
30.2
35.4
±20.2
40.36.5% 71.4% 4,427112,889402 (10.6)1,176 (30.9)1.6Public
Anonymous submission
TLMHT
7. using public detections
16.3
48.7
±8.6
55.315.7% 44.5% 6,63286,504413 (7.9)642 (12.2)4.8Public
TCSVT-02160-2018
HAM_ACT16
8. online method using public detections
26.5
38.1
±8.2
43.37.8% 54.4% 6,976105,434418 (9.9)707 (16.8)8.0Public
LINF1
9. using public detections
26.8
41.0
±9.5
45.711.6% 51.3% 7,89699,224430 (9.4)963 (21.1)4.2Public
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.
DRT
10. online method using public detections
30.7
34.7
±11.4
41.16.3% 61.8% 6,992111,617460 (11.9)1,127 (29.1)6.2Public
Anonymous submission
TrackerAvg RankMOTAIDF1MTMLFPFN ID Sw.FragHzDetector
STAM16
11. online method using public detections
26.0
46.0
±9.1
50.014.6% 43.6% 6,89591,117473 (9.5)1,422 (28.4)0.2Public
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.
TDP
12. online method using public detections
34.1
33.9
±10.2
40.46.2% 62.2% 6,709113,249480 (12.7)1,105 (29.2)9.7Public
Anonymous submission
LTTSC-CRF
13. using public detections
33.8
37.6
±9.9
42.19.6% 55.2% 11,969101,343481 (10.8)1,012 (22.8)0.6Public
N. Le, A. Heili, M. Odobez. Long-Term Time-Sensitive Costs for CRF-Based Tracking by Detection. In ECCVw, 2016.
LMP
14. using public detections
15.5
48.8
±9.8
51.318.2% 40.1% 6,65486,245481 (9.1)595 (11.3)0.5Public
S. Tang, M. Andriluka, B. Andres, B. Schiele. Multiple People Tracking with Lifted Multicut and Person Re-identification. In CVPR, 2017.
RC_cluster
15. using public detections new
11.2
53.8
±8.7
46.921.6% 36.9% 4,76478,948518 (9.1)795 (14.0)8.5Public
Anonymous submission
DMMOT
16. online method using public detections
20.8
46.1
±11.1
54.817.4% 42.7% 7,90989,874532 (10.5)1,616 (31.9)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.
GMPHD_HDA
17. online method using public detections
30.5
30.5
±6.9
33.44.6% 59.7% 5,169120,970539 (16.0)731 (21.7)13.6Public
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.
eHAF16
18. using public detections
18.4
47.2
±16.8
52.418.6% 42.8% 12,58683,107542 (10.0)787 (14.5)0.5Public
TCSVT-02141-2018
TripBFT
19. online method using public detections
19.3
48.3
±8.1
50.915.4% 40.1% 2,70691,047543 (10.8)896 (17.9)0.5Public
Anonymous submission
TripT
20. online method using public detections
19.8
48.1
±8.5
51.915.8% 40.2% 2,82791,210563 (11.3)1,143 (22.9)0.6Public
Anonymous submission
TrackerAvg RankMOTAIDF1MTMLFPFN ID Sw.FragHzDetector
MHT_DAM
21. using public detections
21.3
45.8
±8.9
46.116.2% 43.2% 6,41291,758590 (11.9)781 (15.7)0.8Public
C. Kim, F. Li, A. Ciptadi, J. Rehg. Multiple Hypothesis Tracking Revisited. In ICCV, 2015.
INTERA_MOT
22. using public detections
19.2
45.4
±8.6
47.718.1% 38.7% 13,40785,547600 (11.3)930 (17.5)4.3Public
L. Lan, X. Wang, S. Zhang, D. Tao, W. Gao, T. Huang. Interacting Tracklets for Multi-object Tracking. In IEEE Transactions on Image Processing, 2018.
DWET
23. online method using public detections
33.3
32.2
±10.4
38.36.2% 63.0% 7,297115,780603 (16.5)1,184 (32.4)11.3Public
Anonymous submission
eTC
24. using public detections
15.7
49.2
±9.1
56.117.3% 40.3% 8,40083,702606 (11.2)882 (16.3)0.7Public
Anonymous submission
NLLMPa
25. using public detections
16.3
47.6
±10.6
47.317.0% 40.4% 5,84489,093629 (12.3)768 (15.0)8.3Public
E. Levinkov, J. Uhrig, S. Tang, M. Omran, E. Insafutdinov, A. Kirillov, C. Rother, T. Brox, B. Schiele, B. Andres. Joint Graph Decomposition and Node Labeling: Problem, Algorithms, Applications. In CVPR, 2017.
EDMT
26. using public detections
19.9
45.3
±9.1
47.917.0% 39.9% 11,12287,890639 (12.3)946 (18.3)1.8Public
J. Chen, H. Sheng, Y. Zhang, Z. Xiong. Enhancing Detection Model for Multiple Hypothesis Tracking. In BMTT-PETS CVPRw, 2017.
CEM
27. using public detections
34.0
33.2
±7.9
0.07.8% 54.4% 6,837114,322642 (17.2)731 (19.6)0.3Public
A. Milan, S. Roth, K. Schindler. Continuous Energy Minimization for Multitarget Tracking. In IEEE TPAMI, 2014.
RAR16pub
28. online method using public detections
26.4
45.9
±9.7
48.813.2% 41.9% 6,87191,173648 (13.0)1,992 (39.8)0.9Public
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.
JMC
29. using public detections
20.4
46.3
±9.0
46.315.5% 39.7% 6,37390,914657 (13.1)1,114 (22.2)0.8Public
S. Tang, B. Andres, M. Andriluka, B. Schiele. Multi-Person Tracking by Multicuts and Deep Matching. In BMTT, 2016.
TSN
30. using public detections
21.2
48.2
±8.7
45.719.9% 38.9% 8,44785,315665 (12.5)829 (15.6)0.8Public
Anonymous submission
TrackerAvg RankMOTAIDF1MTMLFPFN ID Sw.FragHzDetector
JCmin_MOT
31. online method using public detections
28.5
36.7
±9.1
36.27.5% 54.4% 2,936111,890667 (17.3)831 (21.5)14.8Public
M. Abhijeet Boragule. Joint Cost Minimization for Multi-Object Tracking. In 2017 IEEE International Conference on Advanced Vide and Signale Based Surveillance, 2017.
CDA_DDALv2
32. online method using public detections
27.3
43.9
±7.8
45.110.7% 44.4% 6,45095,175676 (14.1)1,795 (37.6)0.5Public
S. Bae and K. Yoon, Confidence-Based Data Association and Discriminative Deep Appearance Learning for Robust Online Multi-Object Tracking , In IEEE TPAMI, 2017.
TPM
33. using public detections
20.3
49.1
±9.1
46.920.0% 38.9% 9,03883,031679 (12.5)850 (15.6)0.8Public
Anonymous submission
QuadMOT16
34. using public detections
27.6
44.1
±9.4
38.314.6% 44.9% 6,38894,775745 (15.5)1,096 (22.8)1.8Public
J. Son, M. Baek, M. Cho, B. Han. Multi-Object Tracking with Quadruplet Convolutional Neural Networks. In CVPR, 2017.
MHT_bLSTM6
35. using public detections
27.8
42.1
±9.7
47.814.9% 44.4% 11,63793,172753 (15.4)1,156 (23.6)1.8Public
C. Kim, F. Li, J. Rehg. Multi-object Tracking with Neural Gating Using Bilinear LSTM. In ECCV, 2018.
AMIR
36. online method using public detections
20.3
47.2
±7.7
46.314.0% 41.6% 2,68192,856774 (15.8)1,675 (34.1)1.0Public
A. Sadeghian, A. Alahi, S. Savarese. Tracking The Untrackable: Learning To Track Multiple Cues with Long-Term Dependencies. In ICCV, 2017.
TST_PLS
37. online method using public detections
35.8
39.7
±11.1
43.36.7% 47.4% 8,447100,728783 (17.5)1,730 (38.7)4.0Public
Anonymous submission
AM_ADM
38. online method using public detections
30.0
40.1
±10.1
43.87.1% 46.2% 8,50399,891789 (17.5)1,736 (38.4)5.8Public
Anonymous submission
TBSS
39. online method using public detections
28.0
44.6
±9.3
42.612.3% 43.9% 4,13696,128790 (16.7)1,419 (30.0)3.0Public
X. Zhou, P. Jiang, Z. Wei, H. Dong, F. Wang. Online Multi-Object Tracking with Structural Invariance Constraint. In BMVC, 2018.
MOTDT
40. online method using public detections
20.5
47.6
±8.2
50.915.2% 38.3% 9,25385,431792 (14.9)1,858 (35.0)20.6Public
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.
TrackerAvg RankMOTAIDF1MTMLFPFN ID Sw.FragHzDetector
GCRA
41. using public detections
19.2
48.2
±8.3
48.612.9% 41.1% 5,10488,586821 (16.0)1,117 (21.7)2.8Public
C. Ma, C. Yang, F. Yang, Y. Zhuang, Z. Zhang, H. Jia, X. Xie. Trajectory Factory: Tracklet Cleaving and Re-connection by Deep Siamese Bi-GRU for Multiple Object Tracking. In ICME, 2018.
LFNF16
42. using public detections
28.9
43.6
±11.0
41.613.3% 45.7% 6,61695,363836 (17.5)938 (19.7)0.6Public
Sheng H, Hao L, Chen J, et al. Robust Local Effective Matching Model for Multi-Target Tracking. In PCM, 2017
FWT
43. using public detections
22.0
47.8
±9.4
44.319.1% 38.2% 8,88685,487852 (16.0)1,534 (28.9)0.6Public
R. Henschel, L. Leal-Taixé, D. Cremers, B. Rosenhahn. Fusion of Head and Full-Body Detectors for Multi-Object Tracking. In Trajnet CVPRW, 2018.
deepS2
44. using public detections
26.1
43.6
±8.1
40.415.4% 41.9% 8,81993,095871 (17.8)851 (17.4)0.7Public
ID 32
AFN
45. using public detections
18.0
49.0
±10.2
48.219.1% 35.7% 9,50882,506899 (16.4)1,383 (25.3)0.6Public
Paper ID 4411
LP2D
46. using public detections
31.4
35.7
±10.1
34.28.7% 50.7% 5,084111,163915 (23.4)1,264 (32.4)49.3Public
MOT baseline: Linear programming on 2D image coordinates.
GMMCP
47. using public detections
35.4
38.1
±7.8
35.58.6% 50.9% 6,607105,315937 (22.2)1,669 (39.5)0.5Public
A. Dehghan, S. Assari, M. Shah.. GMMCP-Tracker:Globally Optimal Generalized Maximum Multi Clique Problem for Multiple Object Tracking. In CVPR, 2015.
EAMTT_pub
48. online method using public detections
30.7
38.8
±8.5
42.47.9% 49.1% 8,114102,452965 (22.0)1,657 (37.8)11.8Public
R. Sanchez-Matilla, F. Poiesi, A. Cavallaro "Multi-target tracking with strong and weak detections" in BMTT ECCVw 2016
DCCRF16
49. online method using public detections
27.7
44.8
±9.8
39.714.1% 42.3% 5,61394,133968 (20.0)1,378 (28.5)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.
CSAHD
50. online method using public detections
28.0
43.7
±11.6
45.710.5% 46.1% 8,31893,273984 (20.1)2,164 (44.3)7.0Public
Anonymous submission
TrackerAvg RankMOTAIDF1MTMLFPFN ID Sw.FragHzDetector
PDetTracId
51. online method using public detections
21.7
49.7
±9.4
46.816.7% 37.3% 4,39386,2411,040 (19.7)3,652 (69.3)2.4Public
Anonymous submission
PRT
52. online method using public detections
30.7
40.8
±13.0
44.213.7% 38.3% 15,14391,7921,051 (21.2)2,210 (44.5)6.2Public
Anonymous submission
TBNMF16
53. online method using public detections
32.0
42.0
±9.2
37.510.4% 44.9% 4,96699,7781,085 (24.0)1,400 (30.9)4.5Public
Anonymous submission
JCSTD
54. online method using public detections
24.5
47.4
±8.3
41.114.4% 36.4% 8,07686,6381,266 (24.1)2,697 (51.4)8.8Public
Anonymous submission
OVBT
55. online method using public detections
40.7
38.4
±8.8
37.87.5% 47.3% 11,51799,4631,321 (29.1)2,140 (47.1)0.3Public
Y. Ban, S. Ba, X. Alameda-Pineda, R. Horaud. Tracking Multiple Persons Based on a Variational Bayesian Model. In BMTT 2016, .
CppSORT
56. online method using public detections
35.3
31.5
±9.0
27.74.3% 59.9% 3,048120,2781,587 (46.6)2,239 (65.8)687.1Public
S. Murray. Real-Time Multiple Object Tracking - A Study on the Importance of Speed. In arXiv preprint arXiv:1709.03572, 2017.
MTDF
57. online method using public detections
30.2
45.7
±11.2
40.114.1% 36.4% 12,01884,9701,987 (37.2)3,377 (63.2)1.5Public
Anonymous submission
GCK
58. online method using public detections
40.3
28.7
±8.5
30.63.4% 51.0% 21,436106,4242,217 (53.3)3,277 (78.7)25.1Public
Anonymous submission
HISP_T2
59. online method using public detections
35.1
37.2
±8.6
29.77.6% 50.7% 3,323108,8592,370 (58.8)2,234 (55.4)4.8Public
Anonymous submission
TBD
60. using public detections
42.0
33.7
±9.2
0.07.2% 54.2% 5,804112,5872,418 (63.2)2,252 (58.9)1.3Public
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.
TrackerAvg RankMOTAIDF1MTMLFPFN ID Sw.FragHzDetector
HISP_T
61. online method using public detections
36.7
35.9
±8.5
28.97.8% 50.1% 6,412107,9182,594 (63.6)2,298 (56.3)4.8Public
N. Baisa. Online Multi-target Visual Tracking using a HISP Filter. In Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5: VISAPP,, 2018.
YT16
62. online method using public detections
35.8
37.8
±8.8
31.18.8% 46.1% 4,384106,3652,655 (63.7)2,750 (66.0)12.1Public
Anonymous submission
SMOT
63. using public detections
48.0
29.7
±7.3
0.05.3% 47.7% 17,426107,5523,108 (75.8)4,483 (109.3)0.2Public
C. Dicle, O. Camps, M. Sznaier. The Way They Move: Tracking Targets with Similar Appearance. In ICCV, 2013.
GM_PHD_N1T
64. online method using public detections
37.7
33.3
±8.9
25.55.5% 56.0% 1,750116,4523,499 (96.8)3,594 (99.5)9.9Public
N. Baisa, A. Wallace. Development of a N-type GM-PHD Filter for Multiple Target, Multiple Type Visual Tracking. In CoRR, 2017.

Due to a minor bug in the export script, all results were re-evaluated on April 11, 2016. Here is the old snapshot of the leaderboard.


Benchmark Statistics

SequencesFramesTrajectoriesBoxes
75919759182326

Difficulty Analysis

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

MOT16-03

MOT16-03

(52.1% MOTA)

MOT16-06

MOT16-06

(43.1% MOTA)

MOT16-07

MOT16-07

(37.7% MOTA)

...

...

MOT16-08

MOT16-08

(29.4% MOTA)

MOT16-14

MOT16-14

(23.5% 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.