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 RankMOTAIDF1MTMLFPFNID Sw.FragHzDetector
Adaptation
1. using public detections
12.9
47.6
±10.6
47.417.0% 40.4% 5,78389,168627 (12.3)761 (14.9)2.5Public
Anonymous submission
AFN
2. using public detections
17.5
49.0
±10.2
48.219.1% 35.7% 9,50882,506899 (16.4)1,383 (25.3)0.6Public
Anonymous submission
AMIR
3. online method using public detections
20.5
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.
ARM16
4. using public detections
33.9
35.3
±7.7
40.312.8% 38.5% 23,52092,1712,334 (47.2)3,516 (71.1)5.9Public
Anonymous submission
ASSMOT
5. using public detections
19.6
46.0
±9.3
54.416.6% 42.7% 8,04589,959538 (10.6)1,623 (32.0)0.4Public
Anonymous submission
CDA_DDALv2
6. online method using public detections
29.7
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.
CEM
7. using public detections
37.4
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.
CMRZF
8. using public detections
35.2
30.4
±10.8
29.42.9% 70.5% 1,421124,4831,030 (32.5)733 (23.1)16.9Public
CppSORT
9. online method using public detections
39.3
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.
DCCRF16
10. online method using public detections
29.3
44.8
±9.8
39.714.1% 42.3% 5,61394,133968 (20.0)1,378 (28.5)0.1Public
H. Zhou, W. Ouyang, J. Cheng, X. Wang, and H. Li, Deep Continuous Conditional Random Fields with Asymmetric Inter-object Constraints for Online Multi-object Tracking, IEEE Transactions on Circuits and Systems for Video Technology.
TrackerAvg RankMOTAIDF1MTMLFPFNID Sw.FragHzDetector
DeepS
11. using public detections new
28.9
43.0
±7.8
40.115.3% 41.8% 9,80893,287876 (17.9)865 (17.7)0.5Public
MMSP 2018
DMMOT
12. online method using public detections
18.5
46.1
±11.1
54.817.4% 42.7% 7,90989,874532 (10.5)1,616 (31.9)0.5Public
Anonymous submission
DP_NMS
13. using public detections
32.0
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.
DWET
14. online method using public detections
35.7
32.2
±10.4
38.36.2% 63.0% 7,297115,780603 (16.5)1,184 (32.4)11.3Public
Anonymous submission
EAMTT_pub
15. online method using public detections
35.8
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
EDMT
16. using public detections
18.8
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.
eHAF16
17. using public detections
17.7
47.2
±16.8
52.418.6% 42.8% 12,58683,107542 (10.0)787 (14.5)0.5Public
Anonymous submission
EMOT
18. online method using public detections
30.3
43.0
±8.8
49.213.8% 42.7% 9,52193,672712 (14.6)1,903 (39.1)0.4Public
Anonymous submission
FullTest
19. online method using public detections
32.8
40.7
±32.6
44.811.6% 42.3% 14,35492,6501,136 (23.1)3,864 (78.6)236.8Public
Anonymous submission
FWT
20. using public detections
20.8
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 RankMOTAIDF1MTMLFPFNID Sw.FragHzDetector
GCRA
21. using public detections
18.6
48.2
±8.3
48.612.9% 41.1% 5,10488,586821 (16.0)1,117 (21.7)2.8Public
C.Ma, C.Yang, F.Yang, Y.Zhuang, Z.Zhang, H.Jia, D.Xie. Trajectory Factory: Tracklet Cleaving and Re-connection by Deep Siamese Bi-GRU for Multiple Object Tracking. In ICME 2018.
GMMCP
22. using public detections
39.8
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.
GMPHD_HDA
23. online method using public detections
33.3
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.
GM_PHD_N1T
24. online method using public detections
41.4
33.3
±8.9
25.55.5% 56.0% 1,750116,4523,499 (96.8)3,594 (99.5)9.9Public
N. Baisa, A. Wallace. Development of a N-type GM-PHD Filter for Multiple Target, Multiple Type Visual Tracking. In CoRR, 2017.
HAM_ACT16
25. online method using public detections new
30.1
37.0
±8.3
42.87.9% 56.5% 5,363109,065394 (9.8)819 (20.4)5.8Public
Anonymous submission
HISP_T
26. online method using public detections
41.8
35.9
±8.5
28.97.8% 50.1% 6,412107,9182,594 (63.6)2,298 (56.3)4.8Public
N. Baisa. Online Multi-target Visual Tracking using a HISP Filter. In Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5: VISAPP,, 2018.
HSFSC
27. online method using public detections
30.8
43.3
±9.1
41.612.0% 43.9% 5,55896,996874 (18.7)1,482 (31.7)3.0Public
Anonymous submission
IMWIS
28. using public detections
21.3
47.0
±9.3
41.816.2% 41.4% 4,84290,901868 (17.3)904 (18.0)0.7Public
Anonymous submission
JCmin_MOT
29. online method using public detections
32.3
36.7
±9.1
36.27.5% 54.4% 2,936111,890667 (17.3)831 (21.5)14.8Public
M. Abhijeet Boragule. Joint Cost Minimization for Multi-Object Tracking. In 2017 IEEE International Conference on Advanced Vide and Signale Based Surveillance, 2017.
JMC
30. using public detections
19.7
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.
TrackerAvg RankMOTAIDF1MTMLFPFNID Sw.FragHzDetector
JPDA_m
31. using public detections
33.1
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.
KCF16
32. online method using public detections
21.8
48.8
±9.6
47.215.8% 38.1% 5,87586,567906 (17.3)1,116 (21.2)0.1Public
Paper ID 2988
LFNF16
33. using public detections
30.8
43.6
±11.0
41.613.3% 45.7% 6,61695,363836 (17.5)938 (19.7)0.6Public
Sheng H, Hao L, Chen J, et al. Robust Local Effective Matching Model for Multi-Target Tracking. In PCM, 2017
LINF1
34. using public detections
29.8
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.
LMP
35. using public detections
14.0
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.
LP2D
36. using public detections
35.5
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.
LTTSC-CRF
37. using public detections
37.0
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.
MCjoint
38. using public detections
16.8
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.
MHT_DAM
39. using public detections
21.3
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.
MOTDT
40. online method using public detections
19.9
47.6
±8.2
50.915.2% 38.3% 9,25385,431792 (14.9)1,858 (35.0)20.6Public
Anonymous ICME submission
TrackerAvg RankMOTAIDF1MTMLFPFNID Sw.FragHzDetector
NLLMPa
41. using public detections
15.2
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.
NOMT
42. using public detections
14.7
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.
oICF
43. online method using public detections
30.6
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.
ONEEC
44. online method using public detections
23.9
45.3
±9.5
53.716.3% 42.4% 8,42390,821550 (11.0)1,574 (31.4)0.3Public
Anonymous submission
OVBT
45. online method using public detections
46.4
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, .
overMOT
46. online method using public detections
29.7
43.7
±9.3
50.815.2% 43.0% 8,89193,036662 (13.5)1,844 (37.7)0.4Public
Anonymous submission
OVMOT
47. online method using public detections
32.6
41.9
±8.8
50.115.0% 43.0% 10,71294,510626 (13.0)2,008 (41.7)0.4Public
Anonymous submission
PRMOT
48. using public detections
34.1
43.1
±9.3
44.814.5% 41.8% 10,49592,1741,145 (23.2)1,999 (40.4)0.6Public
Anonymous submission
PRT
49. online method using public detections
33.3
40.8
±13.0
44.213.7% 38.3% 15,14391,7921,051 (21.2)2,210 (44.5)6.2Public
Anonymous submission
PSMT
50. using public detections
32.8
43.9
±9.3
50.715.4% 42.8% 9,10992,271944 (19.1)2,036 (41.2)0.3Public
Anonymous submission
TrackerAvg RankMOTAIDF1MTMLFPFNID Sw.FragHzDetector
QuadMOT16
51. using public detections
29.7
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.
Q_lc
52. online method using public detections
34.0
37.9
±10.3
48.314.2% 37.9% 19,33393,157697 (14.3)1,918 (39.2)0.3Public
Anonymous submission
ReIDT
53. online method using public detections
33.9
40.0
±10.3
43.313.6% 38.1% 17,08891,2411,064 (21.3)2,274 (45.5)6.5Public
Anonymous submission
RMFP
54. online method using public detections
32.7
43.4
±9.2
50.414.9% 44.0% 7,55995,015682 (14.2)1,999 (41.7)0.3Public
Anonymous submission
SAC
55. online method using public detections
30.2
44.6
±9.2
42.712.1% 43.6% 3,92996,285795 (16.8)1,414 (30.0)1.1Public
Anonymous submission
SAD_T
56. online method using public detections
34.3
43.4
±16.2
44.011.7% 59.3% 15,34187,086763 (14.6)1,832 (35.1)11.4Public
Anonymous submission
SDMT
57. online method using public detections
32.9
39.6
±8.3
42.311.7% 49.1% 11,13098,343602 (13.1)772 (16.8)19.8Public
Anonymous submission
SMOT
58. using public detections
53.1
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.
STAM16
59. online method using public detections
27.2
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 2017 IEEE International Conference on Computer Vision (ICCV), 2017.
STbase
60. using public detections
30.1
43.7
±9.2
50.815.2% 43.0% 8,89193,036662 (13.5)1,844 (37.7)0.4Public
Anonymous submission
TrackerAvg RankMOTAIDF1MTMLFPFNID Sw.FragHzDetector
STFP
61. online method using public detections
38.7
39.8
±8.9
47.413.0% 41.4% 12,11896,755950 (20.2)2,630 (56.0)0.4Public
Anonymous submission
STMOT
62. using public detections
25.8
45.4
±9.2
53.316.3% 42.8% 8,07190,883561 (11.2)1,548 (30.9)0.3Public
Anonymous submission
TBD
63. using public detections
46.7
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.
TBNMF16
64. online method using public detections
36.7
42.0
±9.2
37.510.4% 44.9% 4,96699,7781,085 (24.0)1,400 (30.9)4.5Public
Anonymous submission
TBSS
65. online method using public detections
30.5
44.6
±9.3
42.612.3% 43.9% 4,13696,128790 (16.7)1,419 (30.0)3.0Public
Anonymous submission
TPM
66. using public detections
20.1
49.1
±9.1
46.920.0% 38.9% 9,03883,031679 (12.5)850 (15.6)0.8Public
Anonymous submission
TripletT
67. online method using public detections
28.8
44.6
±9.7
48.812.6% 46.6% 2,72597,948422 (9.1)1,093 (23.6)0.1Public
Anonymous submission
TripT
68. online method using public detections
28.4
44.3
±8.1
45.812.5% 46.5% 2,79798,332469 (10.2)1,134 (24.6)0.6Public
Anonymous submission
TTAR
69. using public detections
34.3
42.2
±8.0
37.210.4% 47.8% 4,87299,550909 (20.0)945 (20.8)19.7Public
Anonymous submission
VOFNet
70. online method using public detections
34.6
40.9
±8.3
46.79.7% 47.0% 4,750102,277684 (15.6)4,310 (98.2)24.9Public
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

(51.4% MOTA)

MOT16-06

MOT16-06

(43.8% MOTA)

MOT16-12

MOT16-12

(37.9% MOTA)

...

...

MOT16-08

MOT16-08

(29.5% MOTA)

MOT16-14

MOT16-14

(23.9% 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.
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.