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.Frag HzDetector
CppSORT
1. online method using public detections
37.1
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.
FullTest
2. online method using public detections
29.8
40.7
±32.6
44.811.6% 42.3% 14,35492,6501,136 (23.1)3,864 (78.6)236.8Public
Anonymous submission
LP2D
3. using public detections
33.3
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.
GCK
4. online method using public detections
42.3
28.7
±8.5
30.63.4% 51.0% 21,436106,4242,217 (53.3)3,277 (78.7)25.1Public
Anonymous submission
VOFNet
5. online method using public detections
30.7
40.9
±8.3
46.79.7% 47.0% 4,750102,277684 (15.6)4,310 (98.2)24.9Public
Anonymous submission
JPDA_m
6. using public detections
32.0
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.
MOTDT
7. online method using public detections
20.4
47.6
±8.2
50.915.2% 38.3% 9,25385,431792 (14.9)1,858 (35.0)20.6Public
C. Long, A. Haizhou, Z. Zijie, S. Chong. Real-time Multiple People Tracking with Deeply Learned Candidate Selection and Person Re-identification. In ICME, 2018.
SDMT
8. online method using public detections
29.1
39.6
±8.3
42.311.7% 49.1% 11,13098,343602 (13.1)772 (16.8)19.8Public
Anonymous submission
TTAR
9. using public detections
30.9
42.2
±8.0
37.210.4% 47.8% 4,87299,550909 (20.0)945 (20.8)19.7Public
Anonymous submission
JCmin_MOT
10. online method using public detections
30.0
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.
TrackerAvg RankMOTAIDF1MTMLFPFNID Sw.Frag HzDetector
GMPHD_HDA
11. online method using public detections
32.1
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.
YT16
12. online method using public detections
37.7
37.8
±8.8
31.18.8% 46.1% 4,384106,3652,655 (63.7)2,750 (66.0)12.1Public
Anonymous submission
EAMTT_pub
13. online method using public detections
32.3
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
SAD_T
14. online method using public detections
31.9
43.4
±16.2
44.011.7% 59.3% 15,34187,086763 (14.6)1,832 (35.1)11.4Public
Anonymous submission
DWET
15. online method using public detections
35.3
32.2
±10.4
38.36.2% 63.0% 7,297115,780603 (16.5)1,184 (32.4)11.3Public
Anonymous submission
GM_PHD_N1T
16. online method using public detections
39.6
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.
TDP
17. online method using public detections
36.3
33.9
±10.2
40.46.2% 62.2% 6,709113,249480 (12.7)1,105 (29.2)9.7Public
Anonymous submission
JCSTD
18. online method using public detections
25.3
47.4
±8.3
41.114.4% 36.4% 8,07686,6381,266 (24.1)2,697 (51.4)8.8Public
Anonymous submission
CRC_MB
19. using public detections
37.6
33.6
±10.2
40.17.5% 53.0% 5,882112,5882,633 (68.8)5,483 (143.3)8.5Public
Anonymous submission
NLLMPa
20. using public detections
16.2
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.
TrackerAvg RankMOTAIDF1MTMLFPFNID Sw.Frag HzDetector
HAM_ACT16
21. online method using public detections
28.1
38.1
±8.2
43.37.8% 54.4% 6,976105,434418 (9.9)707 (16.8)8.0Public
Y. Yoon, A. Boragule, Y. Song, K. Yoon, M. Jeon. Online Multi-Object Tracking with Historical Appearance Matching and Scene Adaptive Detection Filtering. In arXiv:1805.10916, 2018.
ReIDT
22. online method using public detections
31.8
40.0
±10.3
43.313.6% 38.1% 17,08891,2411,064 (21.3)2,274 (45.5)6.5Public
Anonymous submission
PRT
23. online method using public detections
31.0
40.8
±13.0
44.213.7% 38.3% 15,14391,7921,051 (21.2)2,210 (44.5)6.2Public
Anonymous submission
DRT
24. online method using public detections
32.8
34.7
±11.4
41.16.3% 61.8% 6,992111,617460 (11.9)1,127 (29.1)6.2Public
Anonymous submission
DP_NMS
25. using public detections
31.1
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.
HISP_T
26. online method using public detections
38.7
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.
TLMHT
27. using public detections
16.1
48.7
±8.6
55.315.7% 44.5% 6,63286,504413 (7.9)642 (12.2)4.8Public
Anonymous submission
TBNMF16
28. online method using public detections
33.2
42.0
±9.2
37.510.4% 44.9% 4,96699,7781,085 (24.0)1,400 (30.9)4.5Public
Anonymous submission
INTERA_MOT
29. using public detections
18.7
45.4
±8.6
47.718.1% 38.7% 13,40785,547600 (11.3)930 (17.5)4.3Public
L. Lan, X. Wang, S. Zhang, D. Tao, W. Gao, T. Huang. Interacting Tracklets for Multi-object Tracking. In IEEE Transactions on Image Processing, 2018.
LINF1
30. 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.
TrackerAvg RankMOTAIDF1MTMLFPFNID Sw.Frag HzDetector
GCRA
31. using public detections
19.2
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.
NOMT
32. using public detections
16.0
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.
Adaptation
33. using public detections
13.8
47.6
±10.6
47.417.0% 40.4% 5,78389,168627 (12.3)761 (14.9)2.5Public
Anonymous submission
QuadMOT16
34. using public detections
28.0
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.
EDMT
35. using public detections
19.5
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.
cm_test
36. online method using public detections new
31.5
35.4
±20.2
40.36.5% 71.4% 4,427112,889402 (10.6)1,176 (30.9)1.6Public
Anonymous submission
AM_ADM
37. online method using public detections
31.9
40.1
±10.1
43.87.1% 46.2% 8,50399,891789 (17.5)1,736 (38.4)1.4Public
Anonymous submission
TBD
38. using public detections
43.4
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.
AMIR
39. 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.
JMC
40. using public detections
20.6
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.Frag HzDetector
MHT_DAM
41. using public detections
21.2
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.
TPM
42. using public detections
19.8
49.1
±9.1
46.920.0% 38.9% 9,03883,031679 (12.5)850 (15.6)0.8Public
Anonymous submission
TSN
43. using public detections
20.7
48.2
±8.7
45.719.9% 38.9% 8,44785,315665 (12.5)829 (15.6)0.8Public
Anonymous submission
TST_PLS
44. online method using public detections
38.0
39.7
±11.1
43.36.7% 47.4% 8,447100,728783 (17.5)1,730 (38.7)0.7Public
Anonymous submission
deepS2
45. using public detections
26.3
43.6
±8.1
40.415.4% 41.9% 8,81993,095871 (17.8)851 (17.4)0.7Public
ID 32
IMWIS
46. using public detections
21.9
47.0
±9.3
41.816.2% 41.4% 4,84290,901868 (17.3)904 (18.0)0.7Public
TCSVT-02160-2018
TripT
47. online method using public detections
19.7
48.1
±8.5
51.915.8% 40.2% 2,82791,210563 (11.3)1,143 (22.9)0.6Public
Anonymous submission
MCjoint
48. using public detections
17.3
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.
FWT
49. using public detections
21.7
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. Fusion of Head and Full-Body Detectors for Multi-Object Tracking. In Trajnet CVPRW, 2018.
LFNF16
50. using public detections
29.3
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
TrackerAvg RankMOTAIDF1MTMLFPFNID Sw.Frag HzDetector
LTTSC-CRF
51. using public detections
35.1
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.
AFN
52. using public detections
18.1
49.0
±10.2
48.219.1% 35.7% 9,50882,506899 (16.4)1,383 (25.3)0.6Public
Anonymous submission
GMMCP
53. using public detections
37.2
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.
TripBFT
54. online method using public detections
18.7
48.3
±8.1
50.915.4% 40.1% 2,70691,047543 (10.8)896 (17.9)0.5Public
Anonymous submission
LMP
55. using public detections
15.2
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.
eHAF16
56. using public detections
18.0
47.2
±16.8
52.418.6% 42.8% 12,58683,107542 (10.0)787 (14.5)0.5Public
TCSVT-02141-2018
CDA_DDALv2
57. online method using public detections
27.6
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.
STbase
58. using public detections
29.8
43.7
±9.2
50.815.2% 43.0% 8,89193,036662 (13.5)1,844 (37.7)0.4Public
Anonymous submission
oICF
59. online method using public detections
28.7
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.
Q_lc
60. online method using public detections
32.0
37.9
±10.3
48.314.2% 37.9% 19,33393,157697 (14.3)1,918 (39.2)0.3Public
Anonymous submission
TrackerAvg RankMOTAIDF1MTMLFPFNID Sw.Frag HzDetector
CEM
61. using public detections
35.7
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.
OVBT
62. online method using public detections
42.0
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, .
SMOT
63. using public detections
49.7
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
64. online method using public detections
26.3
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.
DCCRF16
65. online method using public detections
28.5
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, H. Li. Deep Continuous Conditional Random Fields with Asymmetric Inter-object Constraints for Online Multi-object Tracking. In IEEE Transactions on Circuits and Systems for Video Technology, 2018.
TripletT
66. online method using public detections
26.0
44.6
±9.7
48.812.6% 46.6% 2,72597,948422 (9.1)1,093 (23.6)0.1Public
Anonymous submission
KCF16
67. online method using public detections
21.3
48.8
±9.6
47.215.8% 38.1% 5,87586,567906 (17.3)1,116 (21.2)0.1Public
Paper ID 207

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

(42.1% MOTA)

MOT16-07

MOT16-07

(37.0% MOTA)

...

...

MOT16-08

MOT16-08

(29.0% MOTA)

MOT16-14

MOT16-14

(23.3% 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.
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.