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 Rank MOTAMOTPFAFMTMLFPFNID Sw.FragHzDetector
rookie_ksp
1. using public detections 		32.5
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
2. using public detections 		24.1
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.
tMOT
3. using public detections 		26.7
28.9
±10.5
75.10.65.8% 63.0% 3,754125,494468 (15.0)694 (22.3)11.8Public
Anonymous submission
SMOT
4. using public detections 		43.5
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.
CMRZF
5. using public detections 		26.1
30.4
±10.8
77.80.22.9% 70.5% 1,421124,4831,030 (32.5)733 (23.1)16.9Public
Anonymous submission
GMPHD_HDA
6. 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.
GMPHD_AM
7. online method using public detections 		31.2
30.6
±6.7
74.80.85.9% 53.1% 4,982120,698930 (27.5)1,856 (54.9)7.9Public
Anonymous submission
PAOT
8. online method using public detections 		27.8
31.5
±9.0
77.30.54.3% 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.
GM_PHD_N1T
9. online method using public detections 		30.5
31.6
±8.6
76.60.85.5% 55.2% 4,767115,6454,348 (118.9)3,986 (109.0)9.9Public
Anonymous submission
LKDeep
10. online method using public detections 		34.2
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 Rank MOTAMOTPFAFMTMLFPFNID Sw.FragHzDetector
DP_NMS
11. 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.
ERCTracker
12. online method using public detections 		24.3
32.3
±9.4
76.40.25.7% 62.1% 1,193121,333953 (28.5)943 (28.2)32.0Public
Anonymous submission
CEM
13. using public detections 		29.4
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.
DQNTracker
14. online method using public detections 		31.7
33.7
±13.7
75.40.96.9% 59.3% 5,210113,8651,744 (46.4)4,184 (111.4)9.9Public
Anonymous submission
TBD
15. using public detections 		30.8
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.
EMOT
16. online method using public detections 		32.8
35.7
±7.9
73.42.811.6% 43.7% 16,56699,794826 (18.2)2,123 (46.9)5,919.0Public
Anonymous submission
LP2D
17. using public detections 		24.1
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.
LTTSC-CRF
18. using public detections 		30.8
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.
STMOT
19. using public detections 		32.2
38.2
±8.6
74.02.412.9% 42.8% 13,97097,3841,342 (28.8)2,828 (60.7)5,919.0Public
Anonymous submission
GMCSS
20. online method using public detections 		35.6
38.3
±9.0
74.32.89.4% 46.6% 16,49195,303735 (15.4)2,122 (44.5)0.4Public
Anonymous submission
TrackerAvg Rank MOTAMOTPFAFMTMLFPFNID Sw.FragHzDetector
OVBT
21. online method using public detections 		36.2
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
22. using public detections 		26.9
38.4
±8.9
75.42.712.4% 47.3% 15,76495,796741 (15.6)885 (18.6)2,959.5Public
Anonymous submission
EAMTT_pub
23. online method using public detections 		30.9
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
DeepAC
24. online method using public detections 		28.9
38.8
±9.3
74.90.99.1% 42.8% 5,444103,1742,886 (66.5)6,592 (151.9)21.1Public
Anonymous submission
MOT_M_hun
25. using public detections 		25.3
39.0
±10.3
75.52.613.7% 40.1% 15,34595,029843 (17.6)1,790 (37.4)5,919.0Public
Anonymous submission
dmot
26. using public detections 		27.9
40.7
±8.3
75.21.612.0% 44.1% 9,31997,992773 (16.7)1,106 (23.9)6.6Public
Anonymous submission
LINF1
27. using public detections 		26.2
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.
SLT
28. online method using public detections 		27.7
41.5
±10.4
75.31.411.7% 37.0% 8,07496,9561,705 (36.4)3,170 (67.7)9.6Public
Anonymous submission
oBot
29. online method using public detections 		28.1
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
HFCLP
30. using public detections 		24.2
42.7
±7.6
76.31.412.9% 40.2% 8,50294,2661,676 (34.7)1,792 (37.1)19.7Public
Anonymous submission
TrackerAvg Rank MOTAMOTPFAFMTMLFPFNID Sw.FragHzDetector
oICF
31. online method using public detections 		27.2
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.
CDA_DDALv2
32. online method using public detections 		28.0
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
33. using public detections 		20.2
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.
DCCRF16
34. online method using public detections 		23.2
44.8
±9.5
75.60.914.1% 42.3% 5,61394,125968 (20.0)1,378 (28.5)0.1Public
Anonymous submission
NHL
35. using public detections 		29.5
45.1
±8.5
72.52.115.9% 37.3% 12,60585,6911,747 (33.0)2,033 (38.4)0.3Public
Anonymous submission
EDMT
36. using public detections 		20.1
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.
HAF16
37. using public detections 		21.8
45.7
±8.9
76.01.715.4% 41.4% 10,03888,319660 (12.8)985 (19.1)0.7Public
Anonymous submission
MHT_DAM
38. using public detections 		17.4
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.
RAR16pub
39. online method using public detections 		24.3
45.9
±9.7
74.81.213.2% 41.9% 6,87191,173648 (13.0)1,992 (39.8)0.9Public
Anonymous ICCV submission
STAM16
40. online method using public detections 		24.0
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.
TrackerAvg Rank MOTAMOTPFAFMTMLFPFNID Sw.FragHzDetector
JMC
41. using public detections 		17.6
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.
NOMT
42. using public detections 		14.4
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.
MCjoint
43. using public detections 		16.1
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.
AMIR
44. online method using public detections 		17.5
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.
JCSTD
45. online method using public detections 		23.9
47.4
±8.3
74.41.414.4% 36.4% 8,07786,6311,266 (24.1)2,696 (51.4)3.3Public
Anonymous submission
EAGS16
46. using public detections 		15.3
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
NLLMPa
47. using public detections 		11.7
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.
FWT
48. 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. A Novel Multi-Detector Fusion Framework for Multi-Object Tracking. In arXiv preprint arXiv:1705.08314, 2017.
LMP
49. using public detections 		12.2
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.
HCC
50. using public detections 		9.0
49.3
±10.2
79.00.917.8% 39.9% 5,33386,795391 (7.5)535 (10.2)0.8Public
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

(51.6% MOTA)

MOT16-06

MOT16-06

(44.1% MOTA)

MOT16-07

MOT16-07

(38.6% MOTA)

...

...

MOT16-08

MOT16-08

(29.4% MOTA)

MOT16-14

MOT16-14

(24.3% 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.



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