MOT Challenge - Results

Click on a measure to sort the table accordingly. See below for a more detailed description.

Showing only entries that use public detections!

SELECT * FROM `MOT_results` INNER JOIN `MOT` ON `MOT`.`id` = `MOT_results`.`id_MOT` INNER JOIN `MOT_challenge` ON `MOT_challenge`.`id_MOT` = `MOT`.`id` WHERE `MOT_results`.`chl` = `MOT_challenge`.`challenge` AND `MOT_results`.`sub`=`MOT_challenge`.`nsub` AND `MOT_results`.`chl` ='5' AND `MOT_challenge`.public = '1' AND `MOT`.blocked=0 AND MOT.detector = '0'AND `MOT_results`.`sequence`='OVERALL' ORDER BY MOTA DESC

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

Tracker	MOTA	IDF1	MOTP	MT	ML	FP	FN	Recall	Precision	FAF	ID Sw.	Frag	Hz
DPMOT 1.	63.7 ±12.1	66.2 ±9.4	81.4	196 (25.8)	214 (28.2)	4,829	60,331	66.9	96.2	0.8	1,008 (15.1)	4,319 (64.5)	236.8
DPMOT 1.	Anonymous submission
UnsupTrack 2.	62.4 ±14.6	58.5 ±9.8	78.3	205 (27.0)	242 (31.9)	5,909	61,981	66.0	95.3	1.0	588 (8.9)	1,361 (20.6)	1.9
UnsupTrack 2.	S. Karthik, A. Prabhu, V. Gandhi. Simple Unsupervised Multi-Object Tracking. In Arxiv, 2020.
track2020 3.	62.4 ±11.0	64.0 ±7.8	73.6	285 (37.5)	157 (20.7)	17,523	50,155	72.5	88.3	3.0	883 (12.2)	2,002 (27.6)	29.6
track2020 3.	Anonymous submission
XJTU 4.	62.0 ±11.2	63.8 ±7.8	73.6	286 (37.7)	157 (20.7)	18,308	50,039	72.6	87.8	3.1	909 (12.5)	2,009 (27.7)	5.9
XJTU 4.	Anonymous submission
M_track 5.	61.7 ±11.7	62.1 ±8.6	77.6	259 (34.1)	174 (22.9)	11,702	57,431	68.5	91.4	2.0	729 (10.6)	1,217 (17.8)	4.4
M_track 5.	Anonymous submission
Lif_T 6.	61.3 ±0.0	64.7 ±0.0	78.3	205 (27.0)	258 (34.0)	4,844	65,401	64.1	96.0	0.8	389 (6.1)	1,034 (16.1)	0.5
Lif_T 6.	A. Hornakova, R. Henschel, B. Rosenhahn, P. Swoboda. Lifted Disjoint Paths with Application in Multiple Object Tracking. In ICML, 2020.
MIFTv2 7.	60.4 ±9.4	56.3 ±8.7	77.6	193 (25.4)	220 (29.0)	6,369	65,071	64.3	94.8	1.1	766 (11.9)	961 (14.9)	5.9
MIFTv2 7.
ISE_MOT16 8.	60.1 ±9.2	56.9 ±7.5	77.6	198 (26.1)	221 (29.1)	6,964	65,044	64.3	94.4	1.2	739 (11.5)	951 (14.8)	6.9
ISE_MOT16 8.	MIFT
msot 9.	59.5 ±9.0	52.0 ±8.1	78.1	187 (24.6)	224 (29.5)	5,070	68,001	62.7	95.8	0.9	800 (12.8)	1,014 (16.2)	1.1
msot 9.	Anonymous submission
MPNTrack 10.	58.6 ±10.3	61.7 ±7.3	78.9	207 (27.3)	258 (34.0)	4,949	70,252	61.5	95.8	0.8	354 (5.8)	684 (11.1)	6.5
MPNTrack 10.	G. Brasó, L. Leal-Taixé. Learning a Neural Solver for Multiple Object Tracking. In CVPR, 2020.
Tracker	MOTA	IDF1	MOTP	MT	ML	FP	FN	Recall	Precision	FAF	ID Sw.	Frag	Hz
Lif_TsimInt 11.	57.5 ±9.2	64.1 ±6.0	79.1	193 (25.4)	263 (34.7)	4,249	72,868	60.0	96.3	0.7	335 (5.6)	604 (10.1)	5.9
Lif_TsimInt 11.	A. Hornakova, R. Henschel, B. Rosenhahn, P. Swoboda. Lifted Disjoint Paths with Application in Multiple Object Tracking. In ICML, 2020.
VAN 12.	57.3 ±10.8	57.5 ±8.9	79.1	188 (24.8)	257 (33.9)	3,845	73,489	59.7	96.6	0.6	550 (9.2)	772 (12.9)	4.7
VAN 12.	Anonymous submission
GSM_Tracktor 13.	57.0 ±10.7	58.2 ±9.5	78.1	167 (22.0)	262 (34.5)	4,332	73,573	59.6	96.2	0.7	475 (8.0)	859 (14.4)	7.6
GSM_Tracktor 13.	Q. Qiankun Liu, N. Yu. GSM: Graph Similarity Model for Multi-Object Tracking. In IJCAI, 2020.
Tracktor++v2 14.	56.2 ±11.4	54.9 ±9.9	79.2	157 (20.7)	272 (35.8)	2,394	76,844	57.9	97.8	0.4	617 (10.7)	1,068 (18.5)	1.6
Tracktor++v2 14.	P. Bergmann, T. Meinhardt, L. Leal-Taixé. Tracking without bells and whistles. In ICCV, 2019.
TrctrD16 15.	54.8 ±11.8	53.4 ±9.1	77.5	145 (19.1)	281 (37.0)	2,955	78,765	56.8	97.2	0.5	645 (11.4)	1,515 (26.7)	1.6
TrctrD16 15.	Y. Xu, A. Osep, Y. Ban, R. Horaud, L. Leal-Taixe, X. Alameda-Pineda. How To Train Your Deep Multi-Object Tracker. In , 2019.
Tracktor++ 16.	54.4 ±12.0	52.5 ±9.6	78.2	144 (19.0)	280 (36.9)	3,280	79,149	56.6	96.9	0.6	682 (12.1)	1,480 (26.2)	1.5
Tracktor++ 16.	P. Bergmann, T. Meinhardt, L. Leal-Taixé. Tracking without bells and whistles. In ICCV, 2019.
GNN_tracktor 17.	53.9 ±11.1	53.7 ±10.6	77.0	127 (16.7)	299 (39.4)	2,514	80,892	55.6	97.6	0.4	631 (11.3)	894 (16.1)	2.2
GNN_tracktor 17.	Anonymous submission
HDTR 18.	53.6 ±8.7	46.6 ±6.8	80.8	161 (21.2)	281 (37.0)	4,714	79,353	56.5	95.6	0.8	618 (10.9)	833 (14.7)	3.6
HDTR 18.	M. Babaee, A. Athar, G. Rigoll. Multiple People Tracking Using Hierarchical Deep Tracklet Re-identification. In arXiv preprint arXiv:1811.04091, 2018.
MLT 19.	52.8 ±8.2	62.6 ±6.8	76.1	160 (21.1)	322 (42.4)	5,362	80,444	55.9	95.0	0.9	299 (5.4)	702 (12.6)	5.9
MLT 19.	Y. Zhang, H. Sheng, Y. Wu, S. Wang, W. Ke, Z. Xiong. Multiplex Labeling Graph for Near Online Tracking in Crowded Scenes. In IEEE Internet of Things Journal, 2020.
PV 20.	50.4 ±10.3	50.8 ±7.7	77.7	113 (14.9)	295 (38.9)	2,600	86,780	52.4	97.4	0.4	1,061 (20.2)	3,181 (60.7)	7.3
PV 20.	X. S. Li, Y. T. Liu, K. F. Wang. Multi-Target Tracking with Trajectory Prediction and Re-Identification//2019 Chinese Automation Congress. IEEE.
Tracker	MOTA	IDF1	MOTP	MT	ML	FP	FN	Recall	Precision	FAF	ID Sw.	Frag	Hz
TTL16 21.	50.4 ±10.3	50.1 ±7.4	76.3	132 (17.4)	303 (39.9)	8,491	81,156	55.5	92.3	1.4	807 (14.5)	1,251 (22.5)	2.2
TTL16 21.	Anonymous submission
CRF_TRACK 22.	50.3 ±7.9	54.4 ±6.4	74.8	139 (18.3)	271 (35.7)	7,148	82,746	54.6	93.3	1.2	702 (12.9)	1,387 (25.4)	1.5
CRF_TRACK 22.	Jun xiang, Chao Ma, Guohan Xu, Jianhua Hou, End-to-End Learning Deep CRF models for Multi-Object Tracking. In IEEE Transactions on Circuits and Systems for Video Technology, 2020
ENFT16 23.	50.3 ±8.2	55.0 ±6.4	76.2	146 (19.2)	302 (39.8)	8,341	81,843	55.1	92.3	1.4	490 (8.9)	754 (13.7)	0.4
ENFT16 23.	BUAA
TLO 24.	50.1 ±9.9	48.1 ±7.5	76.5	124 (16.3)	309 (40.7)	5,582	84,629	53.6	94.6	0.9	786 (14.7)	1,294 (24.1)	25.3
TLO 24.	Anonymous submission
CMT16 25.	49.8 ±9.0	59.2 ±6.5	76.1	126 (16.6)	331 (43.6)	9,229	81,882	55.1	91.6	1.6	365 (6.6)	617 (11.2)	6.3
CMT16 25.	#Submission: TIP-21190-2019
NOTA 26.	49.8 ±8.3	55.3 ±5.4	74.5	136 (17.9)	286 (37.7)	7,248	83,614	54.1	93.2	1.2	614 (11.3)	1,372 (25.3)	19.2
NOTA 26.	L. Chen, H. Ai, R. Chen, Z. Zhuang. Aggregate Tracklet Appearance Features for Multi-Object Tracking. In IEEE Signal Processing Letters, 2019.
siameseCos 27.	49.4 ±8.1	49.8 ±7.4	75.9	145 (19.1)	299 (39.4)	6,281	85,384	53.2	93.9	1.1	679 (12.8)	823 (15.5)	0.8
siameseCos 27.	In preparation
S_H_T 28.	49.3 ±10.8	50.7 ±7.1	77.5	158 (20.8)	242 (31.9)	16,998	74,142	59.3	86.4	2.9	1,302 (21.9)	2,183 (36.8)	0.9
S_H_T 28.	Anonymous submission
HCC 29.	49.3 ±10.2	50.7 ±7.4	79.0	135 (17.8)	303 (39.9)	5,333	86,795	52.4	94.7	0.9	391 (7.5)	535 (10.2)	0.8
HCC 29.	L. Ma, S. Tang, M. Black, L. Gool. Customized Multi-Person Tracker. In Computer Vision -- ACCV 2018, 2018.
LSST16O 30.	49.2 ±10.2	56.5 ±7.2	74.0	102 (13.4)	314 (41.4)	7,187	84,875	53.4	93.1	1.2	606 (11.3)	2,497 (46.7)	2.0
LSST16O 30.	Multi-Object Tracking with Multiple Cues and Switcher-Aware Classification
Tracker	MOTA	IDF1	MOTP	MT	ML	FP	FN	Recall	Precision	FAF	ID Sw.	Frag	Hz
eTC 31.	49.2 ±9.1	56.1 ±7.2	75.5	131 (17.3)	306 (40.3)	8,400	83,702	54.1	92.2	1.4	606 (11.2)	882 (16.3)	0.7
eTC 31.	G. Wang, Y. Wang, H. Zhang, R. Gu, J. Hwang. Exploit the connectivity: Multi-object tracking with trackletnet. In Proceedings of the 27th ACM International Conference on Multimedia, 2019.
AFN 32.	49.0 ±10.2	48.2 ±7.4	78.0	145 (19.1)	271 (35.7)	9,508	82,506	54.7	91.3	1.6	899 (16.4)	1,383 (25.3)	0.6
AFN 32.	H. Shen, L. Huang, C. Huang, W. Xu. Tracklet Association Tracker: An End-to-End Learning-based Association Approach for Multi-Object Tracking. In CoRR, 2018.
DAST 33.	48.9 ±8.5	53.2 ±5.5	74.9	115 (15.2)	275 (36.2)	9,987	82,427	54.8	90.9	1.7	838 (15.3)	1,936 (35.3)	8.7
DAST 33.	Anonymous submission
KCF16 34.	48.8 ±9.6	47.2 ±7.8	75.7	120 (15.8)	289 (38.1)	5,875	86,567	52.5	94.2	1.0	906 (17.3)	1,116 (21.2)	0.1
KCF16 34.	P. Chu, H. Fan, C. Tan, H. Ling. Online Multi-Object Tracking with Instance-Aware Tracker and Dynamic Model Refreshment. In WACV, 2019.
LMP 35.	48.8 ±8.9	51.3 ±6.8	79.0	138 (18.2)	304 (40.1)	6,654	86,245	52.7	93.5	1.1	481 (9.1)	595 (11.3)	0.5
LMP 35.	S. Tang, M. Andriluka, B. Andres, B. Schiele. Multiple People Tracking with Lifted Multicut and Person Re-identification. In CVPR, 2017.
TLMHT 36.	48.7 ±8.6	55.3 ±5.6	76.4	119 (15.7)	338 (44.5)	6,632	86,504	52.6	93.5	1.1	413 (7.9)	642 (12.2)	4.8
TLMHT 36.	H. Sheng, J. Chen, Y. Zhang, W. Ke, Z. Xiong, J. Yu. Iterative Multiple Hypothesis Tracking with Tracklet-level Association. In IEEE Transactions on Circuits and Systems for Video Technology, 2018.
STRN_MOT16 37.	48.5 ±8.5	53.9 ±7.1	73.7	129 (17.0)	265 (34.9)	9,038	84,178	53.8	91.6	1.5	747 (13.9)	2,919 (54.2)	13.5
STRN_MOT16 37.	J. Xu, Y. Cao, Z. Zhang, H. Hu. Spatial-Temporal Relation Networks for Multi-Object Tracking. In ICCV, 2019.
GCRA 38.	48.2 ±8.3	48.6 ±5.6	77.5	98 (12.9)	312 (41.1)	5,104	88,586	51.4	94.8	0.9	821 (16.0)	1,117 (21.7)	2.8
GCRA 38.	C. Ma, C. Yang, F. Yang, Y. Zhuang, Z. Zhang, H. Jia, X. Xie. Trajectory Factory: Tracklet Cleaving and Re-connection by Deep Siamese Bi-GRU for Multiple Object Tracking. In ICME, 2018.
FWT 39.	47.8 ±10.0	44.3 ±11.0	75.5	145 (19.1)	290 (38.2)	8,886	85,487	53.1	91.6	1.5	852 (16.0)	1,534 (28.9)	0.6
FWT 39.	R. Henschel, L. Leal-Taixé, D. Cremers, B. Rosenhahn. Fusion of Head and Full-Body Detectors for Multi-Object Tracking. In Trajnet CVPRW, 2018.
MOTDT 40.	47.6 ±8.4	50.9 ±5.5	74.8	115 (15.2)	291 (38.3)	9,253	85,431	53.1	91.3	1.6	792 (14.9)	1,858 (35.0)	20.6
MOTDT 40.	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.
Tracker	MOTA	IDF1	MOTP	MT	ML	FP	FN	Recall	Precision	FAF	ID Sw.	Frag	Hz
NLLMPa 41.	47.6 ±10.6	47.3 ±9.6	78.5	129 (17.0)	307 (40.4)	5,844	89,093	51.1	94.1	1.0	629 (12.3)	768 (15.0)	8.3
NLLMPa 41.	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.
EAGS16 42.	47.4 ±8.6	50.1 ±7.4	75.9	131 (17.3)	324 (42.7)	8,369	86,931	52.3	91.9	1.4	575 (11.0)	913 (17.5)	197.3
EAGS16 42.	H. Sheng, X. Zhang, Y. Zhang, Y. Wu, J. Chen. Enhanced Association with Supervoxels in Multiple Hypothesis Tracking. In IEEE Access, 2018.
JCSTD 43.	47.4 ±8.3	41.1 ±5.6	74.4	109 (14.4)	276 (36.4)	8,076	86,638	52.5	92.2	1.4	1,266 (24.1)	2,697 (51.4)	8.8
JCSTD 43.	W. Tian, M. Lauer, L. Chen. Online Multi-Object Tracking Using Joint Domain Information in Traffic Scenarios. In IEEE Transactions on Intelligent Transportation Systems, 2019.
ASTT 44.	47.2 ±9.6	44.3 ±8.5	76.1	124 (16.3)	316 (41.6)	4,680	90,877	50.2	95.1	0.8	633 (12.6)	814 (16.2)	0.5
ASTT 44.	Yi Tao el al., “Adaptive Spatio-temporal Model Based Multiple Object Tracking Considering a Moving Camera[C]”, International Conference on Universal Village (UV), 2018.
eHAF16 45.	47.2 ±8.7	52.4 ±7.6	75.7	141 (18.6)	325 (42.8)	12,586	83,107	54.4	88.7	2.1	542 (10.0)	787 (14.5)	0.5
eHAF16 45.	H. Sheng, Y. Zhang, J. Chen, Z. Xiong, J. Zhang. Heterogeneous Association Graph Fusion for Target Association in Multiple Object Tracking. In IEEE Transactions on Circuits and Systems for Video Technology, 2018.
AMIR 46.	47.2 ±8.2	46.3 ±7.2	75.8	106 (14.0)	316 (41.6)	2,681	92,856	49.1	97.1	0.5	774 (15.8)	1,675 (34.1)	1.0
AMIR 46.	A. Sadeghian, A. Alahi, S. Savarese. Tracking The Untrackable: Learning To Track Multiple Cues with Long-Term Dependencies. In ICCV, 2017.
MCjoint 47.	47.1 ±10.3	52.3 ±10.2	76.3	155 (20.4)	356 (46.9)	6,703	89,368	51.0	93.3	1.1	370 (7.3)	598 (11.7)	0.6
MCjoint 47.	}@article{DBLP:journals/corr/KeuperTYABS16, author = {Margret Keuper and Siyu Tang and Zhongjie Yu and Bjoern Andres and Thomas Brox and Bernt Schiele}, title = {A Multi-cut Formulation for Joint Segmentation and Tracking of Multiple Objects}, journal = {CoRR}, volume = {abs/1607.06317}, year = {2016}, url = {http://arxiv.org/abs/1607.06317}, timestamp = {Wed, 07 Jun 2017 14:41:31 +0200}, biburl = {http://dblp.uni-trier.de/rec/bib/journals/corr/KeuperTYABS16}, bibsource = {dblp computer science bibliography, http://dblp.org} }
YOONKJ16 48.	47.0 ±8.1	50.1 ±7.5	75.8	125 (16.5)	317 (41.8)	7,901	88,179	51.6	92.3	1.3	627 (12.1)	945 (18.3)	3.5
YOONKJ16 48.	K. YOON, J. GWAK, Y. SONG, Y. YOON, M. JEON. OneShotDA: Online Multi-object Tracker with One-shot-learning-based Data Association. In IEEE Access, 2020.
NOMT 49.	46.4 ±8.9	53.3 ±7.5	76.6	139 (18.3)	314 (41.4)	9,753	87,565	52.0	90.7	1.6	359 (6.9)	504 (9.7)	2.6
NOMT 49.	W. Choi. Near-Online Multi-target Tracking with Aggregated Local Flow Descriptor. In ICCV, 2015.
JMC 50.	46.3 ±9.4	46.3 ±9.2	75.7	118 (15.5)	301 (39.7)	6,373	90,914	50.1	93.5	1.1	657 (13.1)	1,114 (22.2)	0.8
JMC 50.	S. Tang, B. Andres, M. Andriluka, B. Schiele. Multi-Person Tracking by Multicuts and Deep Matching. In BMTT, 2016.
Tracker	MOTA	IDF1	MOTP	MT	ML	FP	FN	Recall	Precision	FAF	ID Sw.	Frag	Hz
DD_TAMA16 51.	46.2 ±8.4	49.4 ±7.6	75.4	107 (14.1)	334 (44.0)	5,126	92,367	49.3	94.6	0.9	598 (12.1)	1,127 (22.8)	6.5
DD_TAMA16 51.	Y. Yoon, D. Kim, K. Yoon, Y. Song, M. Jeon. Online Multiple Pedestrian Tracking using Deep Temporal Appearance Matching Association. In arXiv:1907.00831, 2019.
DASOT16 52.	46.1 ±9.2	49.4 ±8.8	75.3	111 (14.6)	316 (41.6)	8,222	89,204	51.1	91.9	1.4	802 (15.7)	2,057 (40.3)	9.0
DASOT16 52.	Q. Chu, W. Ouyang, B. Liu, F. Zhu, N. Yu. DASOT: A Unified Framework Integrating Data Association and Single Object Tracking for Online Multi-Object Tracking. In Proceedings of the AAAI Conference on Artificial Intelligence, 2020.
DMAN 53.	46.1 ±9.1	54.8 ±7.0	73.8	132 (17.4)	324 (42.7)	7,909	89,874	50.7	92.1	1.3	532 (10.5)	1,616 (31.9)	0.3
DMAN 53.	J. Zhu, H. Yang, N. Liu, M. Kim, W. Zhang, M. Yang. Online Multi-Object Tracking with Dual Matching Attention Networks. In ECCV, 2018.
STAM16 54.	46.0 ±9.1	50.0 ±8.4	74.9	111 (14.6)	331 (43.6)	6,895	91,117	50.0	93.0	1.2	473 (9.5)	1,422 (28.4)	0.2
STAM16 54.	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.
deepS2 55.	46.0 ±8.2	46.5 ±6.1	76.3	118 (15.5)	323 (42.6)	5,124	92,697	49.2	94.6	0.9	693 (14.1)	759 (15.4)	0.7
deepS2 55.	ID 32
RAR16pub 56.	45.9 ±9.7	48.8 ±7.6	74.8	100 (13.2)	318 (41.9)	6,871	91,173	50.0	93.0	1.2	648 (13.0)	1,992 (39.8)	0.9
RAR16pub 56.	K. Fang, Y. Xiang, X. Li, S. Savarese. Recurrent Autoregressive Networks for Online Multi-Object Tracking. In The IEEE Winter Conference on Applications of Computer Vision (WACV), 2018.
MHT_DAM 57.	45.8 ±8.6	46.1 ±7.6	76.3	123 (16.2)	328 (43.2)	6,412	91,758	49.7	93.4	1.1	590 (11.9)	781 (15.7)	0.8
MHT_DAM 57.	C. Kim, F. Li, A. Ciptadi, J. Rehg. Multiple Hypothesis Tracking Revisited. In ICCV, 2015.
MTDF 58.	45.7 ±10.8	40.1 ±8.7	72.6	107 (14.1)	276 (36.4)	12,018	84,970	53.4	89.0	2.0	1,987 (37.2)	3,377 (63.2)	1.5
MTDF 58.	Z. Fu, F. Angelini, J. Chambers, S. Naqvi. Multi-Level Cooperative Fusion of GM-PHD Filters for Online Multiple Human Tracking. In IEEE Transactions on Multimedia, 2019.
INTERA_MOT 59.	45.4 ±8.1	47.7 ±9.0	74.4	137 (18.1)	294 (38.7)	13,407	85,547	53.1	87.8	2.3	600 (11.3)	930 (17.5)	4.3
INTERA_MOT 59.	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.
EDMT 60.	45.3 ±9.1	47.9 ±7.8	75.9	129 (17.0)	303 (39.9)	11,122	87,890	51.8	89.5	1.9	639 (12.3)	946 (18.3)	1.8
EDMT 60.	J. Chen, H. Sheng, Y. Zhang, Z. Xiong. Enhancing Detection Model for Multiple Hypothesis Tracking. In BMTT-PETS CVPRw, 2017.
Tracker	MOTA	IDF1	MOTP	MT	ML	FP	FN	Recall	Precision	FAF	ID Sw.	Frag	Hz
DCCRF16 61.	44.8 ±9.5	39.7 ±8.2	75.6	107 (14.1)	321 (42.3)	5,613	94,133	48.4	94.0	0.9	968 (20.0)	1,378 (28.5)	0.1
DCCRF16 61.	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.
TBSS 62.	44.6 ±9.3	42.6 ±7.2	75.2	93 (12.3)	333 (43.9)	4,136	96,128	47.3	95.4	0.7	790 (16.7)	1,419 (30.0)	3.0
TBSS 62.	X. Zhou, P. Jiang, Z. Wei, H. Dong, F. Wang. Online Multi-Object Tracking with Structural Invariance Constraint. In BMVC, 2018.
OTCD_1 63.	44.4 ±10.8	45.6 ±10.4	75.4	88 (11.6)	361 (47.6)	5,759	94,927	47.9	93.8	1.0	759 (15.8)	1,787 (37.3)	17.6
OTCD_1 63.	Q. Liu, B. Liu, Y. Wu, W. Li, N. Yu. Real-Time Online Multi-Object Tracking in Compressed Domain. In IEEE Access, 2019.
QuadMOT16 64.	44.1 ±9.4	38.3 ±8.6	76.4	111 (14.6)	341 (44.9)	6,388	94,775	48.0	93.2	1.1	745 (15.5)	1,096 (22.8)	1.8
QuadMOT16 64.	J. Son, M. Baek, M. Cho, B. Han. Multi-Object Tracking with Quadruplet Convolutional Neural Networks. In CVPR, 2017.
CDA_DDALv2 65.	43.9 ±7.8	45.1 ±5.7	74.7	81 (10.7)	337 (44.4)	6,450	95,175	47.8	93.1	1.1	676 (14.1)	1,795 (37.6)	0.5
CDA_DDALv2 65.	S. Bae and K. Yoon, Confidence-Based Data Association and Discriminative Deep Appearance Learning for Robust Online Multi-Object Tracking , In IEEE TPAMI, 2017.
LFNF16 66.	43.6 ±8.4	41.6 ±7.8	76.6	101 (13.3)	347 (45.7)	6,616	95,363	47.7	92.9	1.1	836 (17.5)	938 (19.7)	0.6
LFNF16 66.	Sheng H, Hao L, Chen J, et al. Robust Local Effective Matching Model for Multi-Target Tracking. In PCM, 2017
oICF 67.	43.2 ±10.6	49.3 ±9.1	74.3	86 (11.3)	368 (48.5)	6,651	96,515	47.1	92.8	1.1	381 (8.1)	1,404 (29.8)	0.4
oICF 67.	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_bLSTM6 68.	42.1 ±8.6	47.8 ±7.4	75.9	113 (14.9)	337 (44.4)	11,637	93,172	48.9	88.5	2.0	753 (15.4)	1,156 (23.6)	1.8
MHT_bLSTM6 68.	C. Kim, F. Li, J. Rehg. Multi-object Tracking with Neural Gating Using Bilinear LSTM. In ECCV, 2018.
TestUnsup 69.	41.5 ±9.0	44.9 ±8.8	75.2	104 (13.7)	330 (43.5)	12,596	93,404	48.8	87.6	2.1	643 (13.2)	796 (16.3)	19.7
TestUnsup 69.	Multi Object Tracking using Deep Structural Cost Minimization in Data Association
LINF1 70.	41.0 ±10.1	45.7 ±8.5	74.8	88 (11.6)	389 (51.3)	7,896	99,224	45.6	91.3	1.3	430 (9.4)	963 (21.1)	4.2
LINF1 70.	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.
Tracker	MOTA	IDF1	MOTP	MT	ML	FP	FN	Recall	Precision	FAF	ID Sw.	Frag	Hz
PHD_GSDL16 71.	41.0 ±8.9	43.1 ±6.9	75.9	86 (11.3)	315 (41.5)	6,498	99,257	45.6	92.7	1.1	1,810 (39.7)	3,650 (80.1)	8.3
PHD_GSDL16 71.	Z. Fu, P. Feng, F. Angelini, J. Chambers, S. Naqvi. Particle PHD Filter based Multiple Human Tracking using Online Group-Structured Dictionary Learning. In IEEE Access, 2018.
GMPHD_ReId 72.	40.4 ±9.3	49.7 ±8.5	75.2	85 (11.2)	329 (43.3)	6,572	101,266	44.5	92.5	1.1	792 (17.8)	2,529 (56.9)	31.6
GMPHD_ReId 72.	N. Baisa. Occlusion-robust Online Multi-object Visual Tracking using a GM-PHD Filter with a CNN-based Re-identification. In , 2019.
PMPTracker 73.	40.3 ±11.7	38.2 ±8.4	73.3	79 (10.4)	319 (42.0)	10,071	97,524	46.5	89.4	1.7	1,343 (28.9)	2,764 (59.4)	148.0
PMPTracker 73.	Light version of PTZ camera Mutiple People Tracker
AM_ADM 74.	40.1 ±10.1	43.8 ±9.6	75.4	54 (7.1)	351 (46.2)	8,503	99,891	45.2	90.6	1.4	789 (17.5)	1,736 (38.4)	5.8
AM_ADM 74.	S. Lee, M. Kim, S. Bae, Learning Discriminative Appearance Models for Online Multi-Object Tracking with Appearance Discriminability Measures, In IEEE Access, 2018.
SDMT 75.	39.6 ±8.1	42.3 ±6.6	75.5	89 (11.7)	373 (49.1)	11,130	98,343	46.1	88.3	1.9	602 (13.1)	772 (16.8)	19.8
SDMT 75.	M. Thoreau, N. Kottege. Deep Similarity Metric Learning for Real-Time Pedestrian Tracking. In arXiv, 2018.
EAMTT_pub 76.	38.8 ±8.6	42.4 ±7.4	75.1	60 (7.9)	373 (49.1)	8,114	102,452	43.8	90.8	1.4	965 (22.0)	1,657 (37.8)	11.8
EAMTT_pub 76.	R. Sanchez-Matilla, F. Poiesi, A. Cavallaro "Multi-target tracking with strong and weak detections" in BMTT ECCVw 2016
OVBT 77.	38.4 ±8.6	37.8 ±5.5	75.4	57 (7.5)	359 (47.3)	11,517	99,463	45.4	87.8	1.9	1,321 (29.1)	2,140 (47.1)	0.3
OVBT 77.	Y. Ban, S. Ba, X. Alameda-Pineda, R. Horaud. Tracking Multiple Persons Based on a Variational Bayesian Model. In BMTT 2016, .
GMMCP 78.	38.1 ±7.8	35.5 ±4.5	75.8	65 (8.6)	386 (50.9)	6,607	105,315	42.2	92.1	1.1	937 (22.2)	1,669 (39.5)	0.5
GMMCP 78.	A. Dehghan, S. Assari, M. Shah.. GMMCP-Tracker:Globally Optimal Generalized Maximum Multi Clique Problem for Multiple Object Tracking. In CVPR, 2015.
LTTSC-CRF 79.	37.6 ±8.0	42.1 ±6.6	75.9	73 (9.6)	419 (55.2)	11,969	101,343	44.4	87.1	2.0	481 (10.8)	1,012 (22.8)	0.6
LTTSC-CRF 79.	N. Le, A. Heili, M. Odobez. Long-Term Time-Sensitive Costs for CRF-Based Tracking by Detection. In ECCVw, 2016.
HISP_DAL 80.	37.4 ±8.8	30.5 ±6.8	76.3	58 (7.6)	386 (50.9)	3,222	108,865	40.3	95.8	0.5	2,101 (52.1)	2,151 (53.4)	3.3
HISP_DAL 80.	N. Baisa. Robust Online Multi-target Visual Tracking using a HISP Filter with Discriminative Deep Appearance Learning. In CoRR, 2019.
Tracker	MOTA	IDF1	MOTP	MT	ML	FP	FN	Recall	Precision	FAF	ID Sw.	Frag	Hz
JCmin_MOT 81.	36.7 ±9.1	36.2 ±10.5	75.9	57 (7.5)	413 (54.4)	2,936	111,890	38.6	96.0	0.5	667 (17.3)	831 (21.5)	14.8
JCmin_MOT 81.	M. Abhijeet Boragule. Joint Cost Minimization for Multi-Object Tracking. In 2017 IEEE International Conference on Advanced Vide and Signale Based Surveillance, 2017.
HISP_T 82.	35.9 ±8.7	28.9 ±0.0	76.1	59 (7.8)	380 (50.1)	6,412	107,918	40.8	92.1	1.1	2,594 (63.6)	2,298 (56.3)	4.8
HISP_T 82.	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.
LP2D 83.	35.7 ±12.3	34.2 ±9.3	75.8	66 (8.7)	385 (50.7)	5,084	111,163	39.0	93.3	0.9	915 (23.4)	1,264 (32.4)	49.3
LP2D 83.	MOT baseline: Linear programming on 2D image coordinates.
GM_PHD_DAL 84.	35.1 ±9.1	26.6 ±7.1	76.6	53 (7.0)	390 (51.4)	2,350	111,886	38.6	96.8	0.4	4,047 (104.8)	5,338 (138.2)	3.5
GM_PHD_DAL 84.	N. Baisa. Online Multi-object Visual Tracking using a GM-PHD Filter with Deep Appearance Learning. In 2019 22th International Conference on Information Fusion (FUSION), 2019.
TBD 85.	33.7 ±8.8	0.0 ±0.0	76.5	55 (7.2)	411 (54.2)	5,804	112,587	38.2	92.3	1.0	2,418 (63.2)	2,252 (58.9)	1.3
TBD 85.	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.
GM_PHD_N1T 86.	33.3 ±9.0	25.5 ±8.2	76.8	42 (5.5)	425 (56.0)	1,750	116,452	36.1	97.4	0.3	3,499 (96.8)	3,594 (99.5)	9.9
GM_PHD_N1T 86.	N. Baisa, A. Wallace. Development of a N-type GM-PHD filter for multiple target, multiple type visual tracking. In Journal of Visual Communication and Image Representation, 2019.
CEM 87.	33.2 ±8.8	0.0 ±0.0	75.8	59 (7.8)	413 (54.4)	6,837	114,322	37.3	90.9	1.2	642 (17.2)	731 (19.6)	5,919.0
CEM 87.	A. Milan, S. Roth, K. Schindler. Continuous Energy Minimization for Multitarget Tracking. In IEEE TPAMI, 2014.
CppSORT 88.	31.5 ±9.0	27.7 ±9.7	77.3	33 (4.3)	455 (59.9)	3,048	120,278	34.0	95.3	0.5	1,587 (46.6)	2,239 (65.8)	687.1
CppSORT 88.	S. Murray. Real-Time Multiple Object Tracking - A Study on the Importance of Speed. In arXiv preprint arXiv:1709.03572, 2017.
LM_NN 89.	31.0 ±7.2	31.5 ±8.3	78.4	56 (7.4)	443 (58.4)	2,451	122,649	32.7	96.1	0.4	678 (20.7)	666 (20.3)	3.0
LM_NN 89.	ID NEUCOM-D-18-03230
GMPHD_HDA 90.	30.5 ±6.9	33.4 ±5.4	75.4	35 (4.6)	453 (59.7)	5,169	120,970	33.6	92.2	0.9	539 (16.0)	731 (21.7)	13.6
GMPHD_HDA 90.	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.
Tracker	MOTA	IDF1	MOTP	MT	ML	FP	FN	Recall	Precision	FAF	ID Sw.	Frag	Hz
SMOT 91.	29.7 ±8.8	0.0 ±0.0	75.2	40 (5.3)	362 (47.7)	17,426	107,552	41.0	81.1	2.9	3,108 (75.8)	4,483 (109.3)	0.2
SMOT 91.	C. Dicle, O. Camps, M. Sznaier. The Way They Move: Tracking Targets with Similar Appearance. In ICCV, 2013.
JPDA_m 92.	26.2 ±8.8	0.0 ±0.0	76.3	31 (4.1)	512 (67.5)	3,689	130,549	28.4	93.3	0.6	365 (12.9)	638 (22.5)	22.2
JPDA_m 92.	H. Rezatofighi, A. Milan, Z. Zhang, Q. Shi, A. Dick, I. Reid. Joint Probabilistic Data Association Revisited. In ICCV, 2015.
DP_NMS 93.	26.2 ±9.7	31.2 ±5.1	76.3	31 (4.1)	512 (67.5)	3,689	130,557	28.4	93.3	0.6	365 (12.9)	638 (22.5)	212.6
DP_NMS 93.	H. Pirsiavash, D. Ramanan, C. Fowlkes. Globally-Optimal Greedy Algorithms for Tracking a Variable Number of Objects. In CVPR, 2011.

Sequences	Frames	Trajectories	Boxes
7	5919	759	182326

Difficulty Analysis

Sequence difficulty (from easiest to hardest, measured by average MOTA)

MOT16-03

(54.1 MOTA)

MOT16-06

(45.3 MOTA)

MOT16-12

(40.8 MOTA)

...

MOT16-08

(32.0 MOTA)

MOT16-14

(25.1 MOTA)

Evaluation Measures

Lower is better. Higher is better.

Measure	Better	Perfect	Description
MOTA	higher	100%	Multi-Object Tracking Accuracy (+/- denotes standard deviation across all sequences) [1]. This measure combines three error sources: false positives, missed targets and identity switches.
IDF1	higher	100%	ID F1 Score [2]. The ratio of correctly identified detections over the average number of ground-truth and computed detections.
MOTP	higher	100%	Multi-Object Tracking Precision (+/- denotes standard deviation across all sequences) [1]. The misalignment between the annotated and the predicted bounding boxes.
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).
Recall	higher	100%	Ratio of correct detections to total number of GT boxes.
Precision	higher	100%	Ratio of TP / (TP+FP).
FAF	lower	0	The average number of false alarms per frame.
ID Sw.	lower	0	Number of Identity Switches (ID switch ratio = #ID switches / recall) [3]. Please note that we follow the stricter definition of identity switches as described in the reference
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. The frequency is provided by the authors and not officially evaluated by the MOTChallenge.

Legend

Symbol	Description
	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.
	This method used the provided detection set as input.
	This method used a private detection set as input.
	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.

MOT16 Results

Download all results for this benchmark

Benchmark Statistics

Difficulty Analysis

Evaluation Measures

Legend

References: