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 RankMOTAIDF1MTMLFPFN ID Sw.FragHzDetector
NOMT
1. using public detections
14.4
46.4
±9.9
53.318.3% 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.
DP_NMS
2. using public detections
30.6
26.2
±9.3
31.24.1% 67.5% 3,689130,557365 (12.9)638 (22.5)5.9Public
H. Pirsiavash, D. Ramanan, C. Fowlkes. Globally-Optimal Greedy Algorithms for Tracking a Variable Number of Objects. In CVPR, 2011.
JPDA_m
3. using public detections
32.1
26.2
±6.1
0.04.1% 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.
MCjoint
4. using public detections
16.4
47.1
±10.8
52.320.4% 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.
oICF
5. online method using public detections
29.1
43.2
±10.2
49.311.3% 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.
HCC
6. using public detections
10.2
49.3
±10.2
50.717.8% 39.9% 5,33386,795391 (7.5)535 (10.2)0.8Public
Anonymous submission
LINF1
7. using public detections
27.6
41.0
±9.5
45.711.6% 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.
tMOT
8. using public detections
34.2
28.9
±10.5
32.45.8% 63.0% 3,754125,494468 (15.0)694 (22.3)11.8Public
Anonymous submission
STAM16
9. online method using public detections
26.2
46.0
±9.1
50.014.6% 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.
LTTSC-CRF
10. using public detections
34.4
37.6
±9.9
42.19.6% 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.
TrackerAvg RankMOTAIDF1MTMLFPFN ID Sw.FragHzDetector
LMP
11. using public detections
13.1
48.8
±9.8
51.318.2% 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.
DMMOT
12. online method using public detections new
17.0
46.1
±11.1
54.817.4% 42.7% 7,90989,874532 (10.5)1,616 (31.9)0.5Public
Anonymous submission
ASSMOT
13. using public detections
18.0
46.0
±9.3
54.416.6% 42.7% 8,04589,959538 (10.6)1,623 (32.0)0.4Public
Anonymous submission
GMPHD_HDA
14. online method using public detections
31.0
30.5
±6.9
33.44.6% 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.
STbase
15. using public detections
22.4
45.8
±9.1
55.617.7% 41.9% 9,07289,199547 (10.7)1,538 (30.1)0.4Public
Anonymous submission
ONEEC
16. online method using public detections
23.4
45.3
±9.5
53.716.3% 42.4% 8,42390,821550 (11.0)1,574 (31.4)0.3Public
Anonymous submission
STMOT
17. using public detections
25.0
45.4
±9.2
53.316.3% 42.8% 8,07190,883561 (11.2)1,548 (30.9)0.3Public
Anonymous submission
EAGS16
18. using public detections
12.8
47.4
±10.4
50.117.3% 42.7% 8,36986,931575 (11.0)913 (17.5)197.3Public
#PR-D-17-01373# Enhancing Association Graph with Super-voxel for Multi-target Tracking
MHT_DAM
19. using public detections
21.0
45.8
±8.9
46.116.2% 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.
OVMOT
20. online method using public detections
31.9
41.9
±8.8
50.115.0% 43.0% 10,71294,510626 (13.0)2,008 (41.7)0.4Public
Anonymous submission
TrackerAvg RankMOTAIDF1MTMLFPFN ID Sw.FragHzDetector
NLLMPa
21. using public detections
14.8
47.6
±10.6
47.317.0% 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.
EDMT
22. using public detections
17.9
45.3
±9.1
47.917.0% 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.
CEM
23. using public detections
34.8
33.2
±7.9
0.07.8% 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.
RAR16pub
24. online method using public detections
25.7
45.9
±9.7
48.813.2% 41.9% 6,87191,173648 (13.0)1,992 (39.8)0.9Public
Anonymous ICCV submission
JMC
25. using public detections
18.4
46.3
±9.0
46.315.5% 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.
HAF16
26. using public detections
21.1
45.7
±8.9
47.215.4% 41.4% 10,03888,319660 (12.8)985 (19.1)0.7Public
Anonymous submission
overMOT
27. online method using public detections
28.8
43.7
±9.3
50.815.2% 43.0% 8,89193,036662 (13.5)1,844 (37.7)0.4Public
Anonymous submission
CDA_DDALv2
28. online method using public detections
27.8
43.9
±7.8
45.110.7% 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.
RMFP
29. online method using public detections
31.3
43.4
±9.2
50.414.9% 44.0% 7,55995,015682 (14.2)1,999 (41.7)0.3Public
Anonymous submission
EMOT
30. online method using public detections
28.8
43.0
±8.8
49.213.8% 42.7% 9,52193,672712 (14.6)1,903 (39.1)0.4Public
Anonymous submission
TrackerAvg RankMOTAIDF1MTMLFPFN ID Sw.FragHzDetector
NOSVM
31. using public detections
29.5
43.6
±9.4
51.115.3% 42.8% 9,10692,991718 (14.7)2,084 (42.5)0.4Public
Anonymous submission
GMCSS
32. online method using public detections
35.9
38.3
±9.0
44.79.4% 46.6% 16,49195,303735 (15.4)2,122 (44.5)0.4Public
Anonymous submission
QuadMOT16
33. using public detections
28.2
44.1
±9.4
38.314.6% 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.
dmot
34. using public detections
29.2
40.7
±8.3
40.612.0% 44.1% 9,31997,992773 (16.7)1,106 (23.9)6.6Public
Anonymous submission
AMIR
35. online method using public detections
18.7
47.2
±7.7
46.314.0% 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 ICCV, 2017.
TBSS
36. online method using public detections new
28.6
44.6
±9.3
42.612.3% 43.9% 4,13696,128790 (16.7)1,419 (30.0)3.0Public
Anonymous submission
MOTDT
37. online method using public detections
18.9
47.6
±8.2
50.915.2% 38.3% 9,25385,431792 (14.9)1,858 (35.0)18.5Public
Anonymous submission
SAC
38. online method using public detections
28.4
44.6
±9.2
42.712.1% 43.6% 3,92996,285795 (16.8)1,414 (30.0)1.1Public
Anonymous submission
MOT_M_hun
39. using public detections
30.3
39.0
±10.3
47.513.7% 40.1% 15,34595,029843 (17.6)1,790 (37.4)1.2Public
Anonymous submission
FWT
40. using public detections
18.7
47.8
±9.4
44.319.1% 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.
TrackerAvg RankMOTAIDF1MTMLFPFN ID Sw.FragHzDetector
IMWIS
41. using public detections
19.7
47.0
±9.3
41.816.2% 41.4% 4,84290,901868 (17.3)904 (18.0)0.7Public
Anonymous submission
HSFSC
42. online method using public detections
28.5
43.3
±9.1
41.612.0% 43.9% 5,55896,996874 (18.7)1,482 (31.7)3.0Public
Anonymous submission
LP2D
43. using public detections
31.3
35.7
±10.1
34.28.7% 50.7% 5,084111,163915 (23.4)1,264 (32.4)49.3Public
MOT baseline: Linear programming on 2D image coordinates.
GMPHD_AM
44. online method using public detections
36.1
30.6
±6.7
30.25.9% 53.1% 4,982120,698930 (27.5)1,856 (54.9)7.9Public
Anonymous submission
GMMCP
45. using public detections new
35.8
38.1
±7.8
35.58.6% 50.9% 6,607105,315937 (22.2)1,669 (39.5)0.5Public
A. Dehghan, S. Assari, M. Shah.. GMMCP-Tracker:Globally Optimal Generalized Maximum Multi Clique Problem for Multiple Object Tracking. In CVPR, 2015.
PSMT
46. using public detections
32.2
43.9
±9.3
50.715.4% 42.8% 9,10992,271944 (19.1)2,036 (41.2)0.3Public
Anonymous submission
STFP
47. online method using public detections
36.9
39.8
±8.9
47.413.0% 41.4% 12,11896,755950 (20.2)2,630 (56.0)0.4Public
Anonymous submission
ERCTracker
48. online method using public detections
32.4
32.3
±9.4
29.25.7% 62.1% 1,193121,333953 (28.5)943 (28.2)32.0Public
Anonymous submission
EAMTT_pub
49. online method using public detections
32.4
38.8
±8.5
42.47.9% 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
DCCRF16
50. online method using public detections
26.8
44.8
±9.5
39.714.1% 42.3% 5,61394,125968 (20.0)1,378 (28.5)0.1Public
Anonymous submission
TrackerAvg RankMOTAIDF1MTMLFPFN ID Sw.FragHzDetector
CMRZF
51. using public detections
33.3
30.4
±10.8
29.42.9% 70.5% 1,421124,4831,030 (32.5)733 (23.1)16.9Public
Anonymous submission
PRMOT
52. using public detections
32.5
43.1
±9.3
44.814.5% 41.8% 10,49592,1741,145 (23.2)1,999 (40.4)0.6Public
Anonymous submission
JCSTD
53. online method using public detections
24.4
47.4
±8.3
41.114.4% 36.4% 8,07786,6311,266 (24.1)2,696 (51.4)3.3Public
Anonymous submission
OVBT
54. online method using public detections
43.8
38.4
±8.8
37.87.5% 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, .
oBot
55. online method using public detections
31.8
42.5
±20.4
40.812.6% 40.7% 10,42092,8921,559 (31.8)1,639 (33.4)2.3Public
Anonymous BMVC submission
CppSORT
56. online method using public detections
35.9
31.5
±9.0
27.74.3% 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.
HFCLP
57. using public detections
28.8
42.7
±7.6
35.112.9% 40.2% 8,50294,2661,676 (34.7)1,792 (37.1)19.7Public
Anonymous submission
SLT
58. online method using public detections
33.6
41.5
±10.4
41.611.7% 37.0% 8,07496,9561,705 (36.4)3,170 (67.7)9.6Public
Anonymous submission
DQNTracker
59. online method using public detections
37.4
33.7
±13.7
31.76.9% 59.3% 5,210113,8651,744 (46.4)4,184 (111.4)9.9Public
Anonymous submission
NHL
60. using public detections
31.7
45.1
±8.5
32.315.9% 37.3% 12,60585,6911,747 (33.0)2,033 (38.4)0.3Public
Anonymous submission
TrackerAvg RankMOTAIDF1MTMLFPFN ID Sw.FragHzDetector
ARM16
61. using public detections new
31.4
35.3
±7.7
40.312.8% 38.5% 23,52092,1712,334 (47.2)3,516 (71.1)5.9Public
Anonymous submission
LKDeep
62. online method using public detections
40.1
31.8
±19.3
27.66.2% 53.5% 6,179115,8012,389 (65.5)5,745 (157.5)32.0Public
Anonymous submission
TBD
63. using public detections
43.8
33.7
±9.2
0.07.2% 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.
HISP_T
64. online method using public detections
39.5
35.9
±8.5
0.07.8% 50.1% 6,406107,9052,592 (63.5)2,299 (56.3)4.8Public
Anonymous submission
DeepAC
65. online method using public detections
32.6
38.8
±9.3
33.19.1% 42.8% 5,444103,1742,886 (66.5)6,592 (151.9)21.1Public
Anonymous submission
SMOT
66. using public detections
51.2
29.7
±7.3
0.05.3% 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.
rookie_ksp
67. using public detections
44.5
24.8
±7.7
11.52.4% 66.1% 1,421132,3613,343 (122.0)4,886 (178.3)19.7Public
Anonymous submission
GM_PHD_N1T
68. online method using public detections
40.1
31.6
±8.6
19.75.5% 55.2% 4,767115,6454,348 (118.9)3,986 (109.0)9.9Public
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

(51.3% MOTA)

MOT16-06

MOT16-06

(44.3% MOTA)

MOT16-07

MOT16-07

(38.4% MOTA)

...

...

MOT16-08

MOT16-08

(29.5% MOTA)

MOT16-14

MOT16-14

(24.0% 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.
IDF1 higher 100 % ID F1 Score [2]. The ratio of correctly identified detections over the average number of ground-truth and computed detections.
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 [3].
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] Ristani, E., Solera, F., Zou, R., Cucchiara, R. & Tomasi, C. Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking. In ECCV workshop on Benchmarking Multi-Target Tracking, 2016.
[3] 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.