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
HCC
1. 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
2. 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.
MLMRF_DL61
3. 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
FWT
4. 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.
NLLMPa
5. 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.
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
MDPNN16
7. 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.
NOMT
8. 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.
JMC
9. 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.
RAR16pub
10. 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
TrackerAvg RankMOTAMOTPFAFMTMLFPFNID Sw.FragHzDetector
HAF16
11. 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
EDMT
12. 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
PT_JMC
13. 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
NHL
14. 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
QuadMOT16
15. 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.
CDA_DDALv2
16. 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.
oICF
17. 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.
MHT_DAM
18. 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.
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
TSSRC
20. 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
TrackerAvg RankMOTAMOTPFAFMTMLFPFNID Sw.FragHzDetector
LINF1
21. 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.
MOT_M_hun
22. 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
DeepAC
23. 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
EAMTT_pub
24. 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
OVBT
25. 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, .
PRMOT
26. 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
DACTracker
27. 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
EAGS16
28. 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
LTTSC-CRF
29. 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.
GMCSS
30. 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
TrackerAvg RankMOTAMOTPFAFMTMLFPFNID Sw.FragHzDetector
JCmin_MOT
31. 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
LP2D
32. 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.
EMOT
33. 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
deep_sort
34. 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
tMOT
35. 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
TBD
36. 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.
DQNTracker
37. 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
CEM
38. 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.
ERCTracker
39. 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
DP_NMS
40. 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.
TrackerAvg RankMOTAMOTPFAFMTMLFPFNID Sw.FragHzDetector
LKDeep
41. 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
PAOT
42. 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
LRIM
43. 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
GMPHD_AM
44. 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
GMPHD_HDA
45. 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.
CMRZF
46. 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
SMOT
47. 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.
JPDA_m
48. 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.
rookie_ksp
49. 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
PTBFPT
50. 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

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.