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
MAT 1.	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 1.	MAT: Motion-Aware Multi-Object Tracking
AOST 2.	64.8 ±9.9	66.7 ±9.1	78.8	277 (36.5)	149 (19.6)	15,084	47,941	73.7	89.9	2.5	1,092 (14.8)	2,644 (35.9)	23.6
AOST 2.
XJTU 3.	64.4 ±11.7	63.0 ±9.3	77.9	220 (29.0)	204 (26.9)	5,550	58,554	67.9	95.7	0.9	728 (10.7)	3,608 (53.1)	29.6
XJTU 3.	Multiple Object Tracking by Trajectory Map Regression
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.
Lif_T 5.	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 5.	A. Hornakova, R. Henschel, B. Rosenhahn, P. Swoboda. Lifted Disjoint Paths with Application in Multiple Object Tracking. In ICML, 2020.
ITM 6.	61.1 ±12.6	59.5 ±10.9	77.6	213 (28.1)	194 (25.6)	8,919	61,019	66.5	93.2	1.5	1,052 (15.8)	1,933 (29.1)	1.1
ITM 6.	Pivot Correlation Network with Individualized Tubelets for Efficient Multi-object Tracking
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
IFA_MOT 8.	59.8 ±9.4	48.0 ±6.6	78.3	199 (26.2)	144 (19.0)	9,118	61,617	66.2	93.0	1.5	2,599 (39.3)	4,490 (67.8)	1.8
IFA_MOT 8.
MPNTrack 9.	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 9.	G. Brasó, L. Leal-Taixé. Learning a Neural Solver for Multiple Object Tracking. In CVPR, 2020.
Seed_MOT 10.	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 10.
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.
GSM_Tracktor 12.	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 12.	Q. Qiankun Liu, N. Yu. GSM: Graph Similarity Model for Multi-Object Tracking. In IJCAI, 2020.
GNNMatch 13.	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 13.
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.
VAN_on 16.	54.6 ±10.9	54.2 ±8.2	79.4	147 (19.4)	275 (36.2)	2,307	79,895	56.2	97.8	0.4	619 (11.0)	1,574 (28.0)	8.1
VAN_on 16.
Tracktor++ 17.	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++ 17.	P. Bergmann, T. Meinhardt, L. Leal-Taixé. Tracking without bells and whistles. In ICCV, 2019.
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.
TPM 20.	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 20.	J. Peng, T. Wang, et.al. TPM: Multiple Object Tracking with Tracklet-Plane Matching. In Pattern Recognition, 2020.
Tracker	MOTA	IDF1	MOTP	MT	ML	FP	FN	Recall	Precision	FAF	ID Sw.	Frag	Hz
RFS 21.	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 21.	MTSFS：Online Multi-Object Tracking Based on Salient Feature Selection in Crowded Scenes
HOMI_Tracker 22.	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 22.
PV 23.	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 23.	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 24.	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 24.	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 25.	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 25.	BUAA
CMT16 26.	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 26.	#Submission: TIP-21190-2019
NOTA 27.	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 27.	L. Chen, H. Ai, R. Chen, Z. Zhuang. Aggregate Tracklet Appearance Features for Multi-Object Tracking. In IEEE Signal Processing Letters, 2019.
siameseCos 28.	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 28.	In preparation
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.
KCF16 33.	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 33.	P. Chu, H. Fan, C. Tan, H. Ling. Online Multi-Object Tracking with Instance-Aware Tracker and Dynamic Model Refreshment. In WACV, 2019.
LMP 34.	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 34.	S. Tang, M. Andriluka, B. Andres, B. Schiele. Multiple People Tracking with Lifted Multicut and Person Re-identification. In CVPR, 2017.
TLMHT 35.	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 35.	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 36.	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 36.	J. Xu, Y. Cao, Z. Zhang, H. Hu. Spatial-Temporal Relation Networks for Multi-Object Tracking. In ICCV, 2019.
TSN 37.	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 37.	J. Peng, F. Qiu, et.al. Tracklet Siamese Network with Constrained Clustering for Multiple Object Tracking. In VCIP, 2018.
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.
CS_MOT 49.	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 49.	A Cost Matrix Optimization Method Based on Spatial Constraints under Hungarian Algorithm
NOMT 50.	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 50.	W. Choi. Near-Online Multi-target Tracking with Aggregated Local Flow Descriptor. In ICCV, 2015.
Tracker	MOTA	IDF1	MOTP	MT	ML	FP	FN	Recall	Precision	FAF	ID Sw.	Frag	Hz
JMC 51.	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 51.	S. Tang, B. Andres, M. Andriluka, B. Schiele. Multi-Person Tracking by Multicuts and Deep Matching. In BMTT, 2016.
DD_TAMA16 52.	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 52.	Y. Yoon, D. Kim, Y. Song, K. Yoon, M. Jeon. Online Multiple Pedestrians Tracking using Deep Temporal Appearance Matching Association. In Information Sciences, 2020.
DASOT16 53.	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 53.	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 54.	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 54.	J. Zhu, H. Yang, N. Liu, M. Kim, W. Zhang, M. Yang. Online Multi-Object Tracking with Dual Matching Attention Networks. In ECCV, 2018.
EDR16 55.	46.1 ±8.3	46.2 ±6.0	77.1	106 (14.0)	289 (38.1)	4,418	92,849	49.1	95.3	0.7	1,061 (21.6)	3,102 (63.2)	19.7
EDR16 55.	Z. Fu, X. Lai, S. Naqvi. Enhanced Detection Reliability for Human Tracking Based Video Analytics. In International Conference on Information Fusion (FUSION), 2019.
STAM16 56.	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 56.	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 57.	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 57.	ID 32
RAR16pub 58.	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 58.	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 59.	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 59.	C. Kim, F. Li, A. Ciptadi, J. Rehg. Multiple Hypothesis Tracking Revisited. In ICCV, 2015.
MTDF 60.	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 60.	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.
Tracker	MOTA	IDF1	MOTP	MT	ML	FP	FN	Recall	Precision	FAF	ID Sw.	Frag	Hz
INTERA_MOT 61.	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 61.	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 62.	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 62.	J. Chen, H. Sheng, Y. Zhang, Z. Xiong. Enhancing Detection Model for Multiple Hypothesis Tracking. In BMTT-PETS CVPRw, 2017.
DCCRF16 63.	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 63.	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 64.	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 64.	X. Zhou, P. Jiang, Z. Wei, H. Dong, F. Wang. Online Multi-Object Tracking with Structural Invariance Constraint. In BMVC, 2018.
OTCD_1 65.	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 65.	Q. Liu, B. Liu, Y. Wu, W. Li, N. Yu. Real-Time Online Multi-Object Tracking in Compressed Domain. In IEEE Access, 2019.
QuadMOT16 66.	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 66.	J. Son, M. Baek, M. Cho, B. Han. Multi-Object Tracking with Quadruplet Convolutional Neural Networks. In CVPR, 2017.
CDA_DDALv2 67.	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 67.	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 68.	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 68.	Sheng H, Hao L, Chen J, et al. Robust Local Effective Matching Model for Multi-Target Tracking. In PCM, 2017
oICF 69.	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 69.	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 70.	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 70.	C. Kim, F. Li, J. Rehg. Multi-object Tracking with Neural Gating Using Bilinear LSTM. In ECCV, 2018.
Tracker	MOTA	IDF1	MOTP	MT	ML	FP	FN	Recall	Precision	FAF	ID Sw.	Frag	Hz
TestUnsup 71.	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 71.	Multi Object Tracking using Deep Structural Cost Minimization in Data Association
LINF1 72.	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 72.	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 73.	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 73.	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 74.	40.4 ±9.3	50.1 ±8.6	75.2	87 (11.5)	327 (43.1)	6,569	101,251	44.5	92.5	1.1	789 (17.7)	2,519 (56.6)	31.6
GMPHD_ReId 74.	N. Baisa. Occlusion-robust Online Multi-object Visual Tracking using a GM-PHD Filter with a CNN-based Re-identification. In , 2019.
PMPTracker 75.	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 75.	Light version of PTZ camera Mutiple People Tracker
AM_ADM 76.	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 76.	S. Lee, M. Kim, S. Bae, Learning Discriminative Appearance Models for Online Multi-Object Tracking with Appearance Discriminability Measures, In IEEE Access, 2018.
SDMT 77.	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 77.	M. Thoreau, N. Kottege. Deep Similarity Metric Learning for Real-Time Pedestrian Tracking. In arXiv, 2018.
CDF17 78.	39.3 ±10.4	33.6 ±9.9	74.8	95 (12.5)	310 (40.8)	12,430	93,394	48.8	87.7	2.1	4,934 (101.2)	5,886 (120.7)	9.7
CDF17 78.	Z. Fu, S. Naqvi, J. Chambers. Collaborative Detector Fusion of Data-Driven PHD Filter for Online Multiple Human Tracking. In 2018 21st International Conference on Information Fusion (FUSION), 2018.
EAMTT_pub 79.	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 79.	R. Sanchez-Matilla, F. Poiesi, A. Cavallaro "Multi-target tracking with strong and weak detections" in BMTT ECCVw 2016
OVBT 80.	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 80.	Y. Ban, S. Ba, X. Alameda-Pineda, R. Horaud. Tracking Multiple Persons Based on a Variational Bayesian Model. In BMTT 2016, .
Tracker	MOTA	IDF1	MOTP	MT	ML	FP	FN	Recall	Precision	FAF	ID Sw.	Frag	Hz
GMMCP 81.	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 81.	A. Dehghan, S. Assari, M. Shah.. GMMCP-Tracker:Globally Optimal Generalized Maximum Multi Clique Problem for Multiple Object Tracking. In CVPR, 2015.
LTTSC-CRF 82.	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 82.	N. Le, A. Heili, M. Odobez. Long-Term Time-Sensitive Costs for CRF-Based Tracking by Detection. In ECCVw, 2016.
HISP_DAL 83.	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 83.	N. Baisa. Robust Online Multi-target Visual Tracking using a HISP Filter with Discriminative Deep Appearance Learning. In CoRR, 2019.
JCmin_MOT 84.	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 84.	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 85.	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 85.	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 86.	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 86.	MOT baseline: Linear programming on 2D image coordinates.
GM_PHD_DAL 87.	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 87.	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 88.	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 88.	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 89.	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 89.	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 90.	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 90.	A. Milan, S. Roth, K. Schindler. Continuous Energy Minimization for Multitarget Tracking. In IEEE TPAMI, 2014.
Tracker	MOTA	IDF1	MOTP	MT	ML	FP	FN	Recall	Precision	FAF	ID Sw.	Frag	Hz
CppSORT 91.	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 91.	S. Murray. Real-Time Multiple Object Tracking - A Study on the Importance of Speed. In arXiv preprint arXiv:1709.03572, 2017.
LM_NN 92.	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 92.	ID NEUCOM-D-18-03230
GMPHD_HDA 93.	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 93.	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 94.	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 94.	C. Dicle, O. Camps, M. Sznaier. The Way They Move: Tracking Targets with Similar Appearance. In ICCV, 2013.
JPDA_m 95.	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 95.	H. Rezatofighi, A. Milan, Z. Zhang, Q. Shi, A. Dick, I. Reid. Joint Probabilistic Data Association Revisited. In ICCV, 2015.
DP_NMS 96.	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 96.	H. Pirsiavash, D. Ramanan, C. Fowlkes. Globally-Optimal Greedy Algorithms for Tracking a Variable Number of Objects. In CVPR, 2011.
Fa_m 97.	-15.9 ±112.9	21.2 ±28.5	80.5	152 (20.0)	398 (52.4)	66,233	144,576	20.7	36.3	11.2	522 (25.2)	1,355 (65.4)	33.8
Fa_m 97.

Sequences	Frames	Trajectories	Boxes
7	5919	759	182326

Difficulty Analysis

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

MOT16-03

(53.4 MOTA)

MOT16-06

(45.5 MOTA)

MOT16-12

(41.2 MOTA)

...

MOT16-08

(32.3 MOTA)

MOT16-14

(22.1 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: