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!

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
AGT 1.	74.8 ±12.9	67.2 ±11.2	81.2	342 (45.1)	112 (14.8)	12,173	32,462	82.2	92.5	2.1	1,239 (15.1)	2,647 (32.2)	6.6
AGT 1.	Alibaba DAMO Academy city-brain team
MAT 2.	67.7 ±10.5	69.6 ±8.5	81.0	288 (37.9)	202 (26.6)	6,337	52,234	71.4	95.4	1.1	379 (5.3)	623 (8.7)	11.5
MAT 2.	MAT: Motion-Aware Multi-Object Tracking
AOST 3.	64.9 ±9.9	66.4 ±9.0	79.1	271 (35.7)	147 (19.4)	13,869	48,919	73.2	90.6	2.3	1,234 (16.9)	2,654 (36.3)	23.6
AOST 3.
UnsupTrack 4.	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 4.	S. Karthik, A. Prabhu, V. Gandhi. Simple Unsupervised Multi-Object Tracking. In Arxiv, 2020.
track2020 5.	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 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.
ISE_MOT16 7.	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 7.	MIFT
MPNTrack 8.	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 8.	G. Brasó, L. Leal-Taixé. Learning a Neural Solver for Multiple Object Tracking. In CVPR, 2020.
Seed_MOT 9.	57.7 ±10.5	66.1 ±9.2	77.7	318 (41.9)	148 (19.5)	36,735	39,281	78.5	79.6	6.2	1,099 (14.0)	1,674 (21.3)	591.9
Seed_MOT 9.
Lif_TsimInt 10.	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 10.	A. Hornakova, R. Henschel, B. Rosenhahn, P. Swoboda. Lifted Disjoint Paths with Application in Multiple Object Tracking. In ICML, 2020.
Tracker	MOTA	IDF1	MOTP	MT	ML	FP	FN	Recall	Precision	FAF	ID Sw.	Frag	Hz
GSM_Tracktor 11.	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 11.	Q. Qiankun Liu, N. Yu. GSM: Graph Similarity Model for Multi-Object Tracking. In IJCAI, 2020.
GNNMatch 12.	56.9 ±11.2	55.9 ±10.4	79.1	169 (22.3)	268 (35.3)	3,235	74,784	59.0	97.1	0.5	564 (9.6)	818 (13.9)	0.3
GNNMatch 12.	I. Papakis, A. Sarkar, A. Karpatne. GCNNMatch: Graph Convolutional Neural Networks for Multi-Object Tracking via Sinkhorn Normalization. In , 2020.
Tracktor++v2 13.	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 13.	P. Bergmann, T. Meinhardt, L. Leal-Taixé. Tracking without bells and whistles. In ICCV, 2019.
TrctrD16 14.	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 14.	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++ 15.	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++ 15.	P. Bergmann, T. Meinhardt, L. Leal-Taixé. Tracking without bells and whistles. In ICCV, 2019.
HDTR 16.	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 16.	M. Babaee, A. Athar, G. Rigoll. Multiple People Tracking Using Hierarchical Deep Tracklet Re-identification. In arXiv preprint arXiv:1811.04091, 2018.
MLT 17.	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 17.	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.
TPM 18.	51.3 ±9.0	47.9 ±6.3	75.2	142 (18.7)	310 (40.8)	2,701	85,504	53.1	97.3	0.5	569 (10.7)	707 (13.3)	0.8
TPM 18.	J. Peng, T. Wang, et.al. TPM: Multiple Object Tracking with Tracklet-Plane Matching. In Pattern Recognition, 2020.
RFS 19.	50.9 ±11.8	53.9 ±11.2	73.7	127 (16.7)	298 (39.3)	8,884	79,918	56.2	92.0	1.5	714 (12.7)	1,799 (32.0)	1.0
RFS 19.	MTSFS：Online Multi-Object Tracking Based on Salient Feature Selection in Crowded Scenes
HOMI_Tracker 20.	50.4 ±12.6	47.5 ±8.8	77.8	170 (22.4)	232 (30.6)	18,730	69,800	61.7	85.7	3.2	1,826 (29.6)	3,214 (52.1)	9.9
HOMI_Tracker 20.
Tracker	MOTA	IDF1	MOTP	MT	ML	FP	FN	Recall	Precision	FAF	ID Sw.	Frag	Hz
PV 21.	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 21.	X. S. Li, Y. T. Liu, K. F. Wang. Multi-Target Tracking with Trajectory Prediction and Re-Identification//2019 Chinese Automation Congress. IEEE.
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
CMT16 24.	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 24.	#Submission: TIP-21190-2019
NOTA 25.	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 25.	L. Chen, H. Ai, R. Chen, Z. Zhuang. Aggregate Tracklet Appearance Features for Multi-Object Tracking. In IEEE Signal Processing Letters, 2019.
siameseCos 26.	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 26.	In preparation
HCC 27.	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 27.	L. Ma, S. Tang, M. Black, L. Gool. Customized Multi-Person Tracker. In Computer Vision -- ACCV 2018, 2018.
LSST16O 28.	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 28.	Multi-Object Tracking with Multiple Cues and Switcher-Aware Classification
eTC 29.	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 29.	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 30.	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 30.	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.
Tracker	MOTA	IDF1	MOTP	MT	ML	FP	FN	Recall	Precision	FAF	ID Sw.	Frag	Hz
KCF16 31.	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 31.	P. Chu, H. Fan, C. Tan, H. Ling. Online Multi-Object Tracking with Instance-Aware Tracker and Dynamic Model Refreshment. In WACV, 2019.
LMP 32.	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 32.	S. Tang, M. Andriluka, B. Andres, B. Schiele. Multiple People Tracking with Lifted Multicut and Person Re-identification. In CVPR, 2017.
TLMHT 33.	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 33.	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 34.	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 34.	J. Xu, Y. Cao, Z. Zhang, H. Hu. Spatial-Temporal Relation Networks for Multi-Object Tracking. In ICCV, 2019.
TSN 35.	48.2 ±8.6	45.7 ±7.6	75.0	151 (19.9)	295 (38.9)	8,447	85,315	53.2	92.0	1.4	665 (12.5)	829 (15.6)	0.8
TSN 35.	J. Peng, F. Qiu, et.al. Tracklet Siamese Network with Constrained Clustering for Multiple Object Tracking. In VCIP, 2018.
GCRA 36.	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 36.	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 37.	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 37.	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 38.	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 38.	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.
NLLMPa 39.	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 39.	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 40.	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 40.	H. Sheng, X. Zhang, Y. Zhang, Y. Wu, J. Chen. Enhanced Association with Supervoxels in Multiple Hypothesis Tracking. In IEEE Access, 2018.
Tracker	MOTA	IDF1	MOTP	MT	ML	FP	FN	Recall	Precision	FAF	ID Sw.	Frag	Hz
JCSTD 41.	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 41.	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 42.	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 42.	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 43.	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 43.	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 44.	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 44.	A. Sadeghian, A. Alahi, S. Savarese. Tracking The Untrackable: Learning To Track Multiple Cues with Long-Term Dependencies. In ICCV, 2017.
MCjoint 45.	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 45.	}@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 46.	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 46.	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.
CS_MOT 47.	46.7 ±10.6	51.5 ±10.9	74.0	76 (10.0)	332 (43.7)	5,941	90,566	50.3	93.9	1.0	619 (12.3)	2,981 (59.2)	1.2
CS_MOT 47.	A Cost Matrix Optimization Method Based on Spatial Constraints under Hungarian Algorithm
NOMT 48.	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 48.	W. Choi. Near-Online Multi-target Tracking with Aggregated Local Flow Descriptor. In ICCV, 2015.
JMC 49.	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 49.	S. Tang, B. Andres, M. Andriluka, B. Schiele. Multi-Person Tracking by Multicuts and Deep Matching. In BMTT, 2016.
DD_TAMA16 50.	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 50.	Y. Yoon, D. Kim, Y. Song, K. Yoon, M. Jeon. Online Multiple Pedestrians Tracking using Deep Temporal Appearance Matching Association. In Information Sciences, 2020.
Tracker	MOTA	IDF1	MOTP	MT	ML	FP	FN	Recall	Precision	FAF	ID Sw.	Frag	Hz
DASOT16 51.	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 51.	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 52.	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 52.	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 53.	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 53.	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 54.	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 54.	ID 32
RAR16pub 55.	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 55.	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 56.	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 56.	C. Kim, F. Li, A. Ciptadi, J. Rehg. Multiple Hypothesis Tracking Revisited. In ICCV, 2015.
MTDF 57.	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 57.	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 58.	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 58.	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 59.	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 59.	J. Chen, H. Sheng, Y. Zhang, Z. Xiong. Enhancing Detection Model for Multiple Hypothesis Tracking. In BMTT-PETS CVPRw, 2017.
DCCRF16 60.	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 60.	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.
Tracker	MOTA	IDF1	MOTP	MT	ML	FP	FN	Recall	Precision	FAF	ID Sw.	Frag	Hz
TBSS 61.	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 61.	X. Zhou, P. Jiang, Z. Wei, H. Dong, F. Wang. Online Multi-Object Tracking with Structural Invariance Constraint. In BMVC, 2018.
OTCD_1 62.	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 62.	Q. Liu, B. Liu, Y. Wu, W. Li, N. Yu. Real-Time Online Multi-Object Tracking in Compressed Domain. In IEEE Access, 2019.
QuadMOT16 63.	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 63.	J. Son, M. Baek, M. Cho, B. Han. Multi-Object Tracking with Quadruplet Convolutional Neural Networks. In CVPR, 2017.
CDA_DDALv2 64.	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 64.	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 65.	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 65.	Sheng H, Hao L, Chen J, et al. Robust Local Effective Matching Model for Multi-Target Tracking. In PCM, 2017
oICF 66.	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 66.	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 67.	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 67.	C. Kim, F. Li, J. Rehg. Multi-object Tracking with Neural Gating Using Bilinear LSTM. In ECCV, 2018.
TestUnsup 68.	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 68.	Multi Object Tracking using Deep Structural Cost Minimization in Data Association
LINF1 69.	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 69.	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.
PHD_GSDL16 70.	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 70.	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.
Tracker	MOTA	IDF1	MOTP	MT	ML	FP	FN	Recall	Precision	FAF	ID Sw.	Frag	Hz
GMPHD_ReId 71.	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 71.	N. Baisa. Occlusion-robust Online Multi-object Visual Tracking using a GM-PHD Filter with a CNN-based Re-identification. In , 2019.
PMPTracker 72.	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 72.	Light version of PTZ camera Mutiple People Tracker
AM_ADM 73.	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 73.	S. Lee, M. Kim, S. Bae, Learning Discriminative Appearance Models for Online Multi-Object Tracking with Appearance Discriminability Measures, In IEEE Access, 2018.
SDMT 74.	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 74.	M. Thoreau, N. Kottege. Deep Similarity Metric Learning for Real-Time Pedestrian Tracking. In arXiv, 2018.
EAMTT_pub 75.	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 75.	R. Sanchez-Matilla, F. Poiesi, A. Cavallaro "Multi-target tracking with strong and weak detections" in BMTT ECCVw 2016
OVBT 76.	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 76.	Y. Ban, S. Ba, X. Alameda-Pineda, R. Horaud. Tracking Multiple Persons Based on a Variational Bayesian Model. In BMTT 2016, .
GMMCP 77.	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 77.	A. Dehghan, S. Assari, M. Shah.. GMMCP-Tracker:Globally Optimal Generalized Maximum Multi Clique Problem for Multiple Object Tracking. In CVPR, 2015.
LTTSC-CRF 78.	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 78.	N. Le, A. Heili, M. Odobez. Long-Term Time-Sensitive Costs for CRF-Based Tracking by Detection. In ECCVw, 2016.
HISP_DAL 79.	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 79.	N. Baisa. Robust Online Multi-target Visual Tracking using a HISP Filter with Discriminative Deep Appearance Learning. In CoRR, 2019.
JCmin_MOT 80.	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 80.	M. Abhijeet Boragule. Joint Cost Minimization for Multi-Object Tracking. In 2017 IEEE International Conference on Advanced Vide and Signale Based Surveillance, 2017.
Tracker	MOTA	IDF1	MOTP	MT	ML	FP	FN	Recall	Precision	FAF	ID Sw.	Frag	Hz
HISP_T 81.	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 81.	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 82.	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 82.	MOT baseline: Linear programming on 2D image coordinates.
GM_PHD_DAL 83.	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 83.	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 84.	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 84.	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 85.	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 85.	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 86.	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 86.	A. Milan, S. Roth, K. Schindler. Continuous Energy Minimization for Multitarget Tracking. In IEEE TPAMI, 2014.
CppSORT 87.	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 87.	S. Murray. Real-Time Multiple Object Tracking - A Study on the Importance of Speed. In arXiv preprint arXiv:1709.03572, 2017.
LM_NN 88.	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 88.	ID NEUCOM-D-18-03230
GMPHD_HDA 89.	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 89.	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.
SMOT 90.	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 90.	C. Dicle, O. Camps, M. Sznaier. The Way They Move: Tracking Targets with Similar Appearance. In ICCV, 2013.
Tracker	MOTA	IDF1	MOTP	MT	ML	FP	FN	Recall	Precision	FAF	ID Sw.	Frag	Hz
JPDA_m 91.	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 91.	H. Rezatofighi, A. Milan, Z. Zhang, Q. Shi, A. Dick, I. Reid. Joint Probabilistic Data Association Revisited. In ICCV, 2015.
DP_NMS 92.	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 92.	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

(45.1 MOTA)

MOT16-12

(41.0 MOTA)

...

MOT16-08

(32.2 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: