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 RankMOTAMOTPFAFMT MLFPFNID Sw.FragHzDetector
JCSTD
1. online method using public detections
25.4
47.4
±8.3
74.41.414.4% 36.4% 8,07786,6311,266 (24.1)2,696 (51.4)3.3Public
Anonymous submission
SLT
2. online method using public detections
28.9
41.5
±10.4
75.31.411.7% 37.0% 8,07496,9561,705 (36.4)3,170 (67.7)9.6Public
Anonymous submission
NHL
3. using public detections
30.8
45.1
±8.5
72.52.115.9% 37.3% 12,60585,6911,747 (33.0)2,033 (38.4)0.3Public
Anonymous submission
FWT
4. using public detections
21.7
47.8
±9.4
75.51.519.1% 38.2% 8,88685,487852 (16.0)1,534 (28.9)0.6Public
R. Henschel, L. Leal-Taixé, D. Cremers, B. Rosenhahn. Improvements to Frank-Wolfe optimization for multi-detector multi-object tracking. In arXiv preprint arXiv:1705.08314, 2017.
PT_JMC
5. using public detections
24.3
45.2
±8.4
74.82.117.7% 38.3% 12,20487,081681 (13.0)1,152 (22.1)3.8Public
Anonymous submission
JMC
6. using public detections
18.8
46.3
±9.0
75.71.115.5% 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.
HCC
7. using public detections
9.6
49.3
±10.2
79.00.917.8% 39.9% 5,33386,795391 (7.5)535 (10.2)0.8Public
Anonymous submission
EDMT
8. using public detections
21.2
45.3
±9.1
75.91.917.0% 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.
LMP
9. using public detections
12.8
48.8
±9.8
79.01.118.2% 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.
MOT_M_hun
10. using public detections
27.0
39.0
±10.3
75.52.613.7% 40.1% 15,34595,029843 (17.6)1,790 (37.4)5,919.0Public
Anonymous submission
TrackerAvg RankMOTAMOTPFAFMT MLFPFNID Sw.FragHzDetector
NLLMPa
11. using public detections
12.5
47.6
±10.6
78.51.017.0% 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.
oBot
12. online method using public detections
29.5
42.5
±20.4
75.21.812.6% 40.7% 10,42092,8921,559 (31.8)1,639 (33.4)2.3Public
Anonymous BMVC submission
NOMT
13. using public detections
15.1
46.4
±9.9
76.61.618.3% 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.
HAF16
14. using public detections
22.8
45.7
±8.9
76.01.715.4% 41.4% 10,03888,319660 (12.8)985 (19.1)0.7Public
Anonymous submission
AMIR
15. online method using public detections
18.9
47.2
±7.7
75.80.514.0% 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 arXiv preprint arXiv:1701.01909, 2017.
RAR16pub
16. online method using public detections
25.5
45.9
±9.7
74.81.213.2% 41.9% 6,87191,173648 (13.0)1,992 (39.8)0.9Public
Anonymous ICCV submission
DCCRF16
17. online method using public detections
24.8
44.8
±9.5
75.60.914.1% 42.3% 5,61394,125968 (20.0)1,378 (28.5)0.1Public
Anonymous submission
EAGS16
18. using public detections
16.3
47.4
±10.4
75.91.417.3% 42.7% 8,36986,931575 (11.0)913 (17.5)197.3Public
#MM-007925 Enhancing Association Graph with Super-voxel for Multi-target Tracking
DeepAC
19. online method using public detections
29.3
38.8
±9.3
74.90.99.1% 42.8% 5,444103,1742,886 (66.5)6,592 (151.9)21.1Public
Anonymous submission
MHT_DAM
20. using public detections
18.2
45.8
±8.9
76.31.116.2% 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.
TrackerAvg RankMOTAMOTPFAFMT MLFPFNID Sw.FragHzDetector
STAM16
21. online method using public detections
25.0
46.0
±9.1
74.91.214.6% 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 arXiv preprint arXiv:1708.02843, 2017.
EMOT
22. online method using public detections
33.7
35.7
±7.9
73.42.811.6% 43.7% 16,56699,794826 (18.2)2,123 (46.9)5,919.0Public
Anonymous submission
CDA_DDALv2
23. online method using public detections
29.0
43.9
±7.8
74.71.110.7% 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.
QuadMOT16
24. using public detections
21.4
44.1
±9.4
76.41.114.6% 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.
TSSRC
25. online method using public detections
24.8
42.4
±11.8
76.82.512.8% 44.9% 14,68589,654739 (14.5)1,368 (26.9)16.8Public
Anonymous submission
GMCSS
26. online method using public detections
36.9
38.3
±9.0
74.32.89.4% 46.6% 16,49195,303735 (15.4)2,122 (44.5)0.4Public
Anonymous submission
MCjoint
27. using public detections
16.6
47.1
±10.8
76.31.120.4% 46.9% 6,70389,368370 (7.3)598 (11.7)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.
OVBT
28. online method using public detections
37.2
38.4
±8.8
75.41.97.5% 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, .
PRMOT
29. using public detections
28.1
38.4
±8.9
75.42.712.4% 47.3% 15,76495,796741 (15.6)885 (18.6)2,959.5Public
Anonymous submission
SMOT
30. using public detections
44.5
29.7
±7.3
75.22.95.3% 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.
TrackerAvg RankMOTAMOTPFAFMT MLFPFNID Sw.FragHzDetector
oICF
31. online method using public detections
28.1
43.2
±10.2
74.31.111.3% 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.
EAMTT_pub
32. online method using public detections
32.3
38.8
±8.5
75.11.47.9% 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
LP2D
33. using public detections
24.8
35.7
±10.1
75.80.98.7% 50.7% 5,084111,163915 (23.4)1,264 (32.4)49.3Public
MOT baseline: Linear programming on 2D image coordinates.
LINF1
34. using public detections
26.9
41.0
±9.5
74.81.311.6% 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.
deep_sort
35. online method using public detections
27.9
35.3
±8.8
76.21.38.2% 52.8% 7,750109,563602 (15.1)1,446 (36.2)14.8Public
Anonymous submission
GMPHD_AM
36. online method using public detections
32.6
30.6
±6.7
74.80.85.9% 53.1% 4,982120,698930 (27.5)1,856 (54.9)7.9Public
Anonymous submission
LKDeep
37. online method using public detections
35.1
31.8
±19.3
74.81.06.2% 53.5% 6,179115,8012,389 (65.5)5,745 (157.5)32.0Public
Anonymous submission
TBD
38. using public detections
32.0
33.7
±9.2
76.51.07.2% 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.
CEM
39. using public detections
30.4
33.2
±7.9
75.81.27.8% 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.
JCmin_MOT
40. online method using public detections
23.1
36.7
±9.1
75.90.57.5% 54.4% 2,936111,890667 (17.3)831 (21.5)14.8Public
Joint Cost Minimization for Multi-Object Tracking
TrackerAvg RankMOTAMOTPFAFMT MLFPFNID Sw.FragHzDetector
LTTSC-CRF
41. using public detections
31.3
37.6
±9.9
75.92.09.6% 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.
GM_PHD_N1T
42. online method using public detections
31.6
31.6
±8.6
76.60.85.5% 55.2% 4,767115,6454,348 (118.9)3,986 (109.0)9.9Public
Anonymous submission
DQNTracker
43. online method using public detections
33.0
33.7
±13.7
75.40.96.9% 59.3% 5,210113,8651,744 (46.4)4,184 (111.4)9.9Public
Anonymous submission
GMPHD_HDA
44. online method using public detections
27.7
30.5
±6.9
75.40.94.6% 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.
PAOT
45. online method using public detections
28.9
31.5
±9.0
77.30.54.3% 59.9% 3,048120,2781,587 (46.6)2,239 (65.8)687.1Public
Thesis available in August 2017
DP_NMS
46. using public detections
25.1
32.2
±9.8
76.40.25.4% 62.1% 1,123121,579972 (29.2)944 (28.3)212.6Public
H. Pirsiavash, D. Ramanan, C. Fowlkes. Globally-Optimal Greedy Algorithms for Tracking a Variable Number of Objects. In CVPR, 2011.
ERCTracker
47. online method using public detections
25.2
32.3
±9.4
76.40.25.7% 62.1% 1,193121,333953 (28.5)943 (28.2)32.0Public
Anonymous submission
tMOT
48. using public detections new
28.0
28.5
±10.6
75.10.65.1% 64.8% 3,779126,218424 (13.8)648 (21.1)11.8Public
Anonymous submission
rookie_ksp
49. using public detections
33.3
24.8
±7.7
76.40.22.4% 66.1% 1,421132,3613,343 (122.0)4,886 (178.3)19.7Public
Anonymous submission
JPDA_m
50. using public detections
24.9
26.2
±6.1
76.30.64.1% 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.
TrackerAvg RankMOTAMOTPFAFMT MLFPFNID Sw.FragHzDetector
CMRZF
51. using public detections
26.8
30.4
±10.8
77.80.22.9% 70.5% 1,421124,4831,030 (32.5)733 (23.1)16.9Public
Anonymous submission
PTBFPT
52. using public detections
38.4
10.5
±6.4
66.71.40.1% 90.0% 8,106154,754303 (20.0)293 (19.4)0.0Public
Anonymous submission

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

(50.8% MOTA)

MOT16-06

MOT16-06

(43.1% MOTA)

MOT16-07

MOT16-07

(37.4% MOTA)

...

...

MOT16-08

MOT16-08

(28.7% MOTA)

MOT16-14

MOT16-14

(23.2% 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.