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 RankMOTAMOTPFAFMTMLFPFNID Sw.FragHzDetector
CDA_DDALv2
1. online method using public detections
25.9
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.
CEM
2. using public detections
29.1
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.
CMRZF
3. using public detections
25.2
30.4
±10.8
77.80.22.9% 70.5% 1,421124,4831,030 (32.5)733 (23.1)16.9Public
Anonymous submission
DACTracker
4. online method using public detections
30.8
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
5. online method using public detections
26.2
38.8
±9.3
74.90.99.1% 42.8% 5,444103,1742,886 (66.5)6,592 (151.9)21.1Public
Anonymous submission
deep_sort
6. online method using public detections
30.1
35.4
±9.0
76.41.47.8% 51.5% 8,489108,447779 (19.2)2,682 (66.2)13.2Public
Anonymous submission
DP_NMS
7. using public detections
23.7
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.
DQNTracker
8. online method using public detections
30.4
33.7
±13.7
75.40.96.9% 59.3% 5,210113,8651,744 (46.4)4,184 (111.4)9.9Public
Anonymous submission
DSR
9. using public detections
18.7
42.8
±8.8
77.61.112.8% 45.8% 6,37297,214688 (14.7)756 (16.2)1.4Public
Anonymous submission
EAGS16
10. using public detections
24.7
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
TrackerAvg RankMOTAMOTPFAFMTMLFPFNID Sw.FragHzDetector
EAMTT_pub
11. online method using public detections
29.5
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
EDMT
12. using public detections
19.3
45.3
±9.1
75.91.917.0% 39.9% 11,12287,890639 (12.3)946 (18.3)1.8Public
Anonymous submission
ERCTracker
13. online method using public detections
24.2
32.3
±9.4
76.40.25.7% 62.1% 1,193121,333953 (28.5)943 (28.2)32.0Public
Anonymous submission
FWT
14. using public detections
27.0
44.8
±9.0
74.11.916.9% 37.2% 11,08288,4521,044 (20.3)2,563 (49.8)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.
GMCSS
15. online method using public detections
34.8
37.5
±9.0
75.02.58.2% 48.0% 14,60698,511838 (18.2)2,057 (44.7)0.4Public
Anonymous submission
GMPHD_AM
16. online method using public detections
30.0
30.6
±6.7
74.80.85.9% 53.1% 4,982120,698930 (27.5)1,856 (54.9)7.9Public
Anonymous submission
GMPHD_HDA
17. online method using public detections
25.9
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.
HAF16
18. using public detections
20.1
43.4
±9.0
76.41.314.4% 46.2% 7,95694,819496 (10.3)713 (14.9)0.7Public
Anonymous submission
HCC
19. using public detections
8.5
49.3
±10.2
79.00.917.8% 39.9% 5,33386,795391 (7.5)535 (10.2)0.8Public
Anonymous submission
JCmin_MOT
20. 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 RankMOTAMOTPFAFMTMLFPFNID Sw.FragHzDetector
JMC
21. using public detections
17.5
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.
JPDA_m
22. using public detections
23.2
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.
LINF1
23. using public detections
25.0
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.
LKDeep
24. online method using public detections new
32.4
31.8
±19.3
74.81.06.2% 53.5% 6,179115,8012,389 (65.5)5,745 (157.5)32.0Public
Anonymous submission
LMP
25. using public detections
11.8
48.8
±9.8
79.01.118.2% 40.1% 6,65486,245481 (9.1)595 (11.3)0.5Public
Anonymous submission
LP2D
26. using public detections
23.0
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.
LRIM
27. online method using public detections
30.1
30.9
±15.8
76.10.45.4% 54.9% 2,375119,4804,075 (118.2)5,484 (159.1)10.0Public
Anonymous submission
LTTSC-CRF
28. using public detections
29.7
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.
MCjoint
29. using public detections
15.3
47.1
±10.8
76.31.120.4% 46.9% 6,70389,368370 (7.3)598 (11.7)0.6Public
Anonymous submission
MDPNN16
30. online method using public detections
16.9
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.
TrackerAvg RankMOTAMOTPFAFMTMLFPFNID Sw.FragHzDetector
MHT_DAM
31. using public detections
17.2
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.
MLMRF_DL61
32. using public detections
17.6
48.4
±9.4
74.31.318.2% 39.5% 7,84985,719491 (9.3)873 (16.5)0.7Public
Anonymous submission
NHL
33. using public detections
28.5
45.1
±8.5
72.52.115.9% 37.3% 12,60585,6911,747 (33.0)2,033 (38.4)0.3Public
Anonymous submission
NLLMPa
34. using public detections
11.4
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.
NOMT
35. using public detections
13.3
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.
oBot
36. online method using public detections
26.7
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
oICF
37. online method using public detections
26.0
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.
OVBT
38. online method using public detections
34.7
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, .
PTBFPT
39. using public detections
35.2
10.5
±6.4
66.71.40.1% 90.0% 8,106154,754303 (20.0)293 (19.4)0.0Public
Anonymous submission
PT_JMC
40. using public detections
21.9
45.2
±8.4
74.82.117.7% 38.3% 12,20487,081681 (13.0)1,152 (22.1)3.8Public
Anonymous submission
TrackerAvg RankMOTAMOTPFAFMTMLFPFNID Sw.FragHzDetector
QuadMOT16
41. using public detections
19.0
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.
RAR16pub
42. online method using public detections
22.6
45.9
±9.7
74.81.213.2% 41.9% 6,87191,173648 (13.0)1,992 (39.8)0.9Public
Anonymous ICCV submission
SMMUML
43. using public detections
25.2
43.3
±13.6
74.31.411.9% 42.8% 8,46393,892985 (20.3)1,509 (31.1)182.7Public
Accepted in IEEE International Conference on Multimedia Big Data, 2017. Paper ID: 81.
SMOT
44. using public detections
41.4
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.
TBD
45. using public detections
30.0
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.
tMOT
46. online method using public detections
29.1
34.5
±7.6
74.54.313.0% 42.4% 25,20493,462804 (16.5)1,271 (26.1)11.8Public
Anonymous submission
TSSRC
47. online method using public detections
23.5
42.4
±11.8
76.82.512.8% 44.9% 14,68589,654739 (14.5)1,368 (26.9)16.8Public
Anonymous submission
YGT
48. using public detections
21.5
44.7
±9.0
75.22.118.6% 46.5% 12,49187,855404 (7.8)709 (13.7)0.6Public
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

(49.0% MOTA)

MOT16-06

MOT16-06

(41.7% MOTA)

MOT16-07

MOT16-07

(36.4% MOTA)

...

...

MOT16-08

MOT16-08

(28.2% MOTA)

MOT16-14

MOT16-14

(21.8% 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.