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 RankMOTA MOTPFAFMTMLFPFNID Sw.FragHzDetector
PTBFPT
1. using public detections
36.6
10.5
±6.4
66.71.40.1% 90.0% 8,106154,754303 (20.0)293 (19.4)0.0Public
Anonymous submission
NHL
2. using public detections
28.9
45.1
±8.5
72.52.115.9% 37.3% 12,60585,6911,747 (33.0)2,033 (38.4)0.3Public
Anonymous submission
EMOT
3. online method using public detections
31.4
35.7
±7.9
73.42.811.6% 43.7% 16,56699,794826 (18.2)2,123 (46.9)5,919.0Public
Anonymous submission
MLMRF_DL61
4. using public detections
18.0
48.4
±9.4
74.31.318.2% 39.5% 7,84985,719491 (9.3)873 (16.5)0.7Public
Anonymous submission
oICF
5. online method using public detections
26.2
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.
JCSTD
6. online method using public detections
24.0
47.4
±8.3
74.41.414.4% 36.4% 8,07786,6311,266 (24.1)2,696 (51.4)3.0Public
Anonymous submission
tMOT
7. online method using public detections
30.0
34.5
±7.6
74.54.313.0% 42.4% 25,20493,462804 (16.5)1,271 (26.1)11.8Public
Anonymous submission
CDA_DDALv2
8. online method using public detections
26.6
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.
PT_JMC
9. using public detections
22.6
45.2
±8.4
74.82.117.7% 38.3% 12,20487,081681 (13.0)1,152 (22.1)3.8Public
Anonymous submission
LKDeep
10. online method using public detections
33.6
31.8
±19.3
74.81.06.2% 53.5% 6,179115,8012,389 (65.5)5,745 (157.5)32.0Public
Anonymous submission
TrackerAvg RankMOTA MOTPFAFMTMLFPFNID Sw.FragHzDetector
GMPHD_AM
11. online method using public detections
31.3
30.6
±6.7
74.80.85.9% 53.1% 4,982120,698930 (27.5)1,856 (54.9)7.9Public
Anonymous submission
RAR16pub
12. online method using public detections
23.4
45.9
±9.7
74.81.213.2% 41.9% 6,87191,173648 (13.0)1,992 (39.8)0.9Public
Anonymous ICCV submission
LINF1
13. using public detections
24.8
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.
DACTracker
14. online method using public detections
31.8
38.2
±9.5
74.81.28.4% 45.8% 7,079103,3942,228 (51.5)5,969 (137.9)9.9Public
Anonymous submission
DeepAC
15. online method using public detections
27.4
38.8
±9.3
74.90.99.1% 42.8% 5,444103,1742,886 (66.5)6,592 (151.9)21.1Public
Anonymous submission
GMCSS
16. online method using public detections
34.7
37.5
±9.0
75.02.58.2% 48.0% 14,60698,511838 (18.2)2,057 (44.7)0.4Public
Anonymous submission
EAMTT_pub
17. online method using public detections
30.2
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
SMOT
18. using public detections
42.5
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.
oBot
19. online method using public detections
27.4
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
OVBT
20. online method using public detections
34.9
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, .
TrackerAvg RankMOTA MOTPFAFMTMLFPFNID Sw.FragHzDetector
PRMOT
21. using public detections
25.7
38.4
±8.9
75.42.712.4% 47.3% 15,76495,796741 (15.6)885 (18.6)2,959.5Public
Anonymous submission
GMPHD_HDA
22. online method using public detections
26.6
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.
DQNTracker
23. online method using public detections
31.6
33.7
±13.7
75.40.96.9% 59.3% 5,210113,8651,744 (46.4)4,184 (111.4)9.9Public
Anonymous submission
MOT_M_hun
24. using public detections
24.9
39.0
±10.3
75.52.613.7% 40.1% 15,34595,029843 (17.6)1,790 (37.4)5,919.0Public
Anonymous submission
FWT
25. using public detections
20.2
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.
JMC
26. using public detections
17.7
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.
MDPNN16
27. online method using public detections
17.7
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.
LP2D
28. using public detections
23.5
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.
CEM
29. using public detections
28.9
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
30. online method using public detections
22.2
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 RankMOTA MOTPFAFMTMLFPFNID Sw.FragHzDetector
EDMT
31. using public detections
19.6
45.3
±9.1
75.91.917.0% 39.9% 11,12287,890639 (12.3)946 (18.3)1.8Public
Anonymous submission
LTTSC-CRF
32. using public detections
29.4
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.
HAF16
33. using public detections
21.3
45.7
±8.9
76.01.715.4% 41.4% 10,03888,319660 (12.8)985 (19.1)0.7Public
Anonymous submission
LRIM
34. online method using public detections
31.1
30.9
±15.8
76.10.45.4% 54.9% 2,375119,4804,075 (118.2)5,484 (159.1)10.0Public
Anonymous submission
deep_sort
35. online method using public detections
26.2
35.3
±8.8
76.21.38.2% 52.8% 7,750109,563602 (15.1)1,446 (36.2)14.8Public
Anonymous submission
JPDA_m
36. using public detections
24.2
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.
ERCTracker
37. online method using public detections
24.5
32.3
±9.4
76.40.25.7% 62.1% 1,193121,333953 (28.5)943 (28.2)32.0Public
Anonymous submission
QuadMOT16
38. using public detections
19.6
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.
DP_NMS
39. using public detections
24.2
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.
rookie_ksp
40. using public detections new
32.0
24.8
±7.7
76.40.22.4% 66.1% 1,421132,3613,343 (122.0)4,886 (178.3)19.7Public
Anonymous submission
TrackerAvg RankMOTA MOTPFAFMTMLFPFNID Sw.FragHzDetector
TBD
41. using public detections
31.1
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.
NOMT
42. using public detections
14.2
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.
MHT_DAM
43. using public detections
17.3
42.9
±8.9
76.61.013.6% 46.9% 5,66897,919499 (10.8)659 (14.2)0.8Public
C. Kim, F. Li, A. Ciptadi, J. Rehg. Multiple Hypothesis Tracking Revisited. In ICCV, 2015.
TSSRC
44. online method using public detections
23.2
42.4
±11.8
76.82.512.8% 44.9% 14,68589,654739 (14.5)1,368 (26.9)16.8Public
Anonymous submission
EAGS16
45. using public detections
24.5
37.8
±9.4
76.91.711.9% 47.6% 9,891102,3601,061 (24.2)963 (22.0)197.3Public
#MM-007925 Enhancing Association Graph with Super-voxel for Multi-target Tracking
PAOT
46. online method using public detections new
28.1
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
CMRZF
47. using public detections
25.8
30.4
±10.8
77.80.22.9% 70.5% 1,421124,4831,030 (32.5)733 (23.1)16.9Public
Anonymous submission
NLLMPa
48. using public detections
11.9
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.
HCC
49. using public detections
9.4
49.3
±10.2
79.00.917.8% 39.9% 5,33386,795391 (7.5)535 (10.2)0.8Public
Anonymous submission
LMP
50. using public detections
12.1
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.

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

(49.2% MOTA)

MOT16-06

MOT16-06

(41.7% MOTA)

MOT16-07

MOT16-07

(36.7% MOTA)

...

...

MOT16-08

MOT16-08

(28.0% MOTA)

MOT16-14

MOT16-14

(21.9% 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.