MOT Challenge - Results

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Showing only entries that use public detections!

Benchmark Statistics

Tracker	MOTA	IDF1	HOTA	MT	ML	FP	FN	Rcll	Prcn	AssA	DetA	AssRe	AssPr	DetRe	DetPr	LocA	FAF	ID Sw.	Frag	Hz
Lif_T 1.	52.5	60.0	46.0	244 (33.8)	186 (25.8)	6,837	21,610	64.8	85.3	47.8	44.9	53.7	75.0	51.2	67.4	79.8	1.2	730 (11.3)	1,047 (16.2)	1.5
Lif_T 1.	A. Hornakova, R. Henschel, B. Rosenhahn, P. Swoboda. Lifted Disjoint Paths with Application in Multiple Object Tracking. In ICML, 2020.
MPNTrack 2.	51.5	58.6	45.0	225 (31.2)	187 (25.9)	7,620	21,780	64.6	83.9	46.2	44.4	54.8	67.1	51.0	66.3	79.4	1.3	375 (5.8)	872 (13.5)	6.5
MPNTrack 2.	G. Braso, L. Leal-Taixe. Learning a Neural Solver for Multiple Object Tracking. In CVPR, 2020.
ApLift 3.	51.1	59.0	45.7	284 (39.4)	163 (22.6)	10,070	19,288	68.6	80.7	46.7	45.4	55.2	68.7	54.1	63.7	79.3	1.7	677 (0.0)	1,022 (0.0)	0.6
ApLift 3.	A. Hornakova, T. Kaiser, M. Rolinek, B. Rosenhahn, P. Swoboda, R. Henschel. Making Higher Order MOT Scalable: An Efficient Approximate Solver for Lifted Disjoint Paths. In International Conference on Computer Vision (ICCV), 2021.
mfi_tst 4.	49.2	52.4	41.5	210 (29.1)	176 (24.4)	8,707	21,594	64.9	82.1	40.3	43.4	44.2	74.2	50.7	64.2	79.0	1.5	912 (0.0)	1,397 (0.0)	0.7
mfi_tst 4.	J. Y, H. Ge, J. Yang, Y. Tong, S. Su. Online multi-object tracking using multi-function integration and tracking simulation training. In Applied Intelligence, 2021.
Lif_TsimInt 5.	47.2	57.6	43.8	195 (27.0)	215 (29.8)	7,635	24,277	60.5	83.0	46.7	41.5	52.4	74.5	47.8	65.5	79.4	1.3	554 (9.2)	803 (13.3)	5.8
Lif_TsimInt 5.	A. Hornakova, R. Henschel, B. Rosenhahn, P. Swoboda. Lifted Disjoint Paths with Application in Multiple Object Tracking. In ICML, 2020.
SLA_public 6.	47.0	57.9	43.0	163 (22.6)	196 (27.2)	9,044	22,986	62.6	81.0	44.7	41.9	51.5	68.7	49.1	63.5	78.9	1.6	558 (8.9)	1,580 (25.2)	21.0
SLA_public 6.	Spatial-Attention Location-Aware Multi-Object Tracking. In , 2020.
ISE_MOT15R 7.	46.7	51.6	41.3	212 (29.4)	185 (25.7)	11,003	20,839	66.1	78.7	39.2	44.1	45.1	67.7	53.1	63.2	80.1	1.9	878 (13.3)	1,265 (19.1)	6.7
ISE_MOT15R 7.	MIFT
GNNMatch 8.	46.7	43.2	35.4	157 (21.8)	203 (28.2)	6,643	25,311	58.8	84.5	31.4	40.8	44.2	49.3	46.3	66.6	79.6	1.1	820 (0.0)	1,371 (0.0)	0.1
GNNMatch 8.	I. Papakis, A. Sarkar, A. Karpatne. GCNNMatch: Graph Convolutional Neural Networks for Multi-Object Tracking via Sinkhorn Normalization. In arXiv preprint arXiv:2010.00067, 2020.
Tracktor++v2 9.	46.6	47.6	37.6	131 (18.2)	201 (27.9)	4,624	26,896	56.2	88.2	35.9	40.1	39.7	72.5	44.3	69.5	79.9	0.8	1,290 (22.9)	1,702 (30.3)	1.4
Tracktor++v2 9.	P. Bergmann, T. Meinhardt, L. Leal-Taixé. Tracking without bells and whistles. In ICCV, 2019.
TrctrD15 10.	44.1	46.0	36.3	124 (17.2)	192 (26.6)	6,085	26,917	56.2	85.0	34.5	39.0	38.7	69.5	43.9	66.4	79.0	1.1	1,347 (24.0)	1,868 (33.2)	1.6
TrctrD15 10.	Y. Xu, A. Osep, Y. Ban, R. Horaud, L. Leal-Taixe, X. Alameda-Pineda. How To Train Your Deep Multi-Object Tracker. In , 2019.
Tracker	MOTA	IDF1	HOTA	MT	ML	FP	FN	Rcll	Prcn	AssA	DetA	AssRe	AssPr	DetRe	DetPr	LocA	FAF	ID Sw.	Frag	Hz
Tracktor++ 11.	44.1	46.7	37.1	130 (18.0)	189 (26.2)	6,477	26,577	56.7	84.3	35.7	39.3	40.0	70.3	44.4	65.9	78.8	1.1	1,318 (23.2)	1,790 (31.5)	0.9
Tracktor++ 11.	P. Bergmann, T. Meinhardt, L. Leal-Taixé. Tracking without bells and whistles. In ICCV, 2019.
KDT 12.	41.5	49.8	37.5	131 (18.2)	196 (27.2)	4,606	30,201	50.8	87.2	39.1	36.4	47.4	64.0	39.9	68.4	79.0	0.8	1,105 (0.0)	2,462 (0.0)	65.7
KDT 12.
End_To_End 13.	40.6	51.9	40.1	294 (40.8)	86 (11.9)	18,027	17,352	71.8	71.0	38.2	42.7	47.2	60.3	56.3	55.7	78.3	3.1	1,129 (0.0)	2,166 (0.0)	9.7
End_To_End 13.	A. Boragule, H. Jang, N. Ha, M. Jeon. Pixel-Guided Association for Multi-Object Tracking. In Sensors, 2022.
MOMOT_GLMB 14.	40.0	50.3	36.2	43 (6.0)	266 (36.9)	3,190	33,370	45.7	89.8	39.6	33.6	44.4	70.9	36.1	71.0	80.2	0.6	307 (6.7)	4,492 (98.3)	0.7
MOMOT_GLMB 14.	Mohammadjavad Abbaspour, Mohammad Ali Masnadi-Shirazi. Online Multi-Object Tracking with delta-GLMB Filter based on Occlusion and Identity Switch Handling. In arXiv preprint arXiv:2011.10111, 2020.
CRFTrack_ 15.	40.0	49.6	37.3	166 (23.0)	206 (28.6)	10,295	25,917	57.8	77.5	37.4	37.7	40.9	70.7	44.3	59.4	76.1	1.8	658 (11.4)	1,508 (26.1)	3.2
CRFTrack_ 15.	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
KCF 16.	38.9	44.5	33.1	120 (16.6)	227 (31.5)	7,321	29,501	52.0	81.4	31.3	35.3	35.1	65.1	39.5	61.8	74.7	1.3	720 (13.9)	1,440 (27.7)	0.3
KCF 16.	P. Chu, H. Fan, C. Tan, H. Ling. Online Multi-Object Tracking with Instance-Aware Tracker and Dynamic Model Refreshment. In WACV, 2019.
AP_HWDPL_p 17.	38.5	47.1	35.0	63 (8.7)	270 (37.4)	4,005	33,203	46.0	87.6	37.9	32.9	42.4	65.0	35.4	67.4	76.7	0.7	586 (12.8)	1,263 (27.5)	6.7
AP_HWDPL_p 17.	C. Long, A. Haizhou, S. Chong, Z. Zijie, B. Bo. Online Multi-Object Tracking with Convolutional Neural Networks. In 2017 IEEE International Conference on Image Processing (ICIP), 2017.
STRN 18.	38.1	46.6	33.5	83 (11.5)	241 (33.4)	5,451	31,571	48.6	84.6	33.5	33.8	38.5	58.7	37.1	64.5	76.0	0.9	1,033 (21.2)	2,665 (54.8)	13.8
STRN 18.	J. Xu, Y. Cao, Z. Zhang, H. Hu. Spatial-Temporal Relation Networks for Multi-Object Tracking. In ICCV, 2019.
AMIR15 19.	37.6	46.0	33.2	114 (15.8)	193 (26.8)	7,933	29,397	52.2	80.2	32.2	34.7	36.5	63.2	39.5	60.8	75.8	1.4	1,026 (19.7)	2,024 (38.8)	1.9
AMIR15 19.	A. Sadeghian, A. Alahi, S. Savarese. Tracking The Untrackable: Learning To Track Multiple Cues with Long-Term Dependencies. In ICCV, 2017.
TPM 20.	36.2	43.6	32.7	111 (15.4)	307 (42.6)	5,650	33,102	46.1	83.4	33.7	32.1	37.8	66.3	35.2	63.6	75.6	1.0	420 (9.1)	928 (20.1)	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	HOTA	MT	ML	FP	FN	Rcll	Prcn	AssA	DetA	AssRe	AssPr	DetRe	DetPr	LocA	FAF	ID Sw.	Frag	Hz
SST_MOT15 21.	35.8	39.6	30.7	56 (7.8)	281 (39.0)	4,065	33,669	45.2	87.2	29.9	32.3	33.6	65.0	34.7	67.0	76.5	0.7	1,728 (38.2)	1,312 (29.0)	6.3
SST_MOT15 21.	Shijie Sun, Naveed Akhtar, Ajmal Mian
JointMC 22.	35.6	45.1	34.6	167 (23.2)	283 (39.3)	10,580	28,508	53.6	75.7	34.7	34.9	39.4	64.9	41.1	58.0	75.8	1.8	457 (8.5)	969 (18.1)	0.6
JointMC 22.	M. Keuper, S. Tang, B. Andres, T. Brox, B. Schiele. Motion Segmentation amp; Multiple Object Tracking by Correlation Co-Clustering. In IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018.
TSN 23.	35.5	43.0	32.2	104 (14.4)	314 (43.6)	5,682	33,515	45.5	83.1	33.2	31.7	37.1	66.5	34.7	63.5	75.5	1.0	454 (10.0)	967 (21.3)	0.8
TSN 23.	J. Peng, F. Qiu, et.al. Tracklet Siamese Network with Constrained Clustering for Multiple Object Tracking. In VCIP, 2018.
RAR15pub 24.	35.1	45.4	33.2	94 (13.0)	305 (42.3)	6,771	32,717	46.7	80.9	34.9	31.9	37.8	69.9	35.5	61.4	75.1	1.2	381 (8.1)	1,523 (32.6)	5.4
RAR15pub 24.	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.
mLK 25.	35.1	47.5	33.4	89 (12.3)	276 (38.3)	5,678	33,815	45.0	83.0	36.3	31.0	39.3	70.2	34.1	62.9	75.7	1.0	383 (8.5)	1,175 (26.1)	1.0
mLK 25.	Yuan Zhang, Di Xie and Shiliang Pu (Hikvision Research Institute)
HybridDAT 26.	35.0	47.7	35.1	82 (11.4)	304 (42.2)	8,455	31,140	49.3	78.2	37.1	33.3	41.0	69.2	38.1	60.4	76.3	1.5	358 (7.3)	1,267 (25.7)	4.6
HybridDAT 26.	M. Yang, Y. Jia. A Hybrid Data Association Framework for Robust Online Multi-Object Tracking. In IEEE Transactions on Image Processing, 2016.
INARLA 27.	34.7	42.1	31.8	90 (12.5)	216 (30.0)	9,855	29,158	52.5	76.6	29.5	34.5	33.0	63.3	40.0	58.3	74.8	1.7	1,112 (21.2)	2,848 (54.2)	2.6
INARLA 27.	H. Wu, Y. Hu, K. Wang, H. Li, L. Nie, H. Cheng. Instance-aware representation learning and association for online multi-person tracking. In Pattern Recognition, 2019.
AM 28.	34.3	48.3	34.5	82 (11.4)	313 (43.4)	5,154	34,848	43.3	83.8	39.5	30.4	43.2	69.0	32.9	63.6	74.8	0.9	348 (8.0)	1,463 (33.8)	0.5
AM 28.	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.
TSMLCDEnew 29.	34.3	44.1	0.0	101 (14.0)	284 (39.4)	7,869	31,908	48.1	79.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.4	618 (12.9)	959 (20.0)	6.5
TSMLCDEnew 29.	B. Wang, G. Wang, K. L. Chan, L. Wang. Tracklet Association by Online Target-Specific Metric Learning and Coherent Dynamics Estimation. In arXiv:1511.06654, 2015.
QuadMOT 30.	33.8	40.4	0.0	93 (12.9)	266 (36.9)	7,898	32,061	47.8	78.8	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.4	703 (14.7)	1,430 (29.9)	3.7
QuadMOT 30.	J. Son, M. Baek, M. Cho, B. Han. Multi-Object Tracking with Quadruplet Convolutional Neural Networks. In CVPR, 2017.
Tracker	MOTA	IDF1	HOTA	MT	ML	FP	FN	Rcll	Prcn	AssA	DetA	AssRe	AssPr	DetRe	DetPr	LocA	FAF	ID Sw.	Frag	Hz
NOMT 31.	33.7	44.6	33.7	88 (12.2)	317 (44.0)	7,762	32,547	47.0	78.8	36.3	31.6	40.1	68.9	35.8	60.1	75.8	1.3	442 (9.4)	823 (17.5)	11.5
NOMT 31.	W. Choi. Near-Online Multi-target Tracking with Aggregated Local Flow Descriptor. In ICCV, 2015.
DCCRF 32.	33.6	39.1	29.3	75 (10.4)	271 (37.6)	5,917	34,002	44.7	82.3	28.3	30.9	30.9	69.7	33.9	62.5	75.1	1.0	866 (19.4)	1,566 (35.1)	0.1
DCCRF 32.	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.
TDAM 33.	33.0	46.1	34.1	96 (13.3)	282 (39.1)	10,064	30,617	50.2	75.4	35.6	33.0	38.5	70.8	38.7	58.2	76.5	1.7	464 (9.2)	1,506 (30.0)	5.9
TDAM 33.	M. Yang, Y. Jia. Temporal dynamic appearance modeling for online multi-person tracking. In Computer Vision and Image Understanding, 2016.
CDA_DDALpb 34.	32.8	38.8	28.7	70 (9.7)	304 (42.2)	4,983	35,690	41.9	83.8	28.5	29.3	32.8	61.9	31.7	63.4	74.8	0.9	614 (14.7)	1,583 (37.8)	2.3
CDA_DDALpb 34.	S. Bae and K. Yoon, Confidence-Based Data Association and Discriminative Deep Appearance Learning for Robust Online Multi-Object Tracking , In IEEE TPAMI, 2017.
MHT_DAM 35.	32.4	45.3	33.8	115 (16.0)	316 (43.8)	9,064	32,060	47.8	76.4	36.6	31.5	40.3	69.8	36.4	58.2	75.8	1.6	435 (9.1)	826 (17.3)	0.7
MHT_DAM 35.	C. Kim, F. Li, A. Ciptadi, J. Rehg. Multiple Hypothesis Tracking Revisited. In ICCV, 2015.
LFNF 36.	31.6	33.1	26.1	69 (9.6)	301 (41.7)	5,943	35,095	42.9	81.6	23.5	29.4	25.4	70.7	32.6	62.0	76.1	1.0	961 (22.4)	1,106 (25.8)	4.0
LFNF 36.	Sheng H, Hao L, Chen J, et al. Robust Local Effective Matching Model for Multi-Target Tracking. In PCM, 2017
EDR15 37.	30.8	41.6	32.3	80 (11.1)	253 (35.1)	11,531	30,264	50.7	73.0	31.2	33.8	36.2	63.0	40.4	58.1	77.8	2.0	743 (14.6)	2,283 (45.0)	12.9
EDR15 37.	Z. Fu, X. Lai, S. Naqvi. Enhanced Detection Reliability for Human Tracking Based Video Analytics. In 2019 22th International Conference on Information Fusion (FUSION), 2019.
GMPHD_OGM 38.	30.7	38.8	11.7	83 (11.5)	275 (38.1)	6,502	35,030	43.0	80.2	30.9	4.5	35.5	61.0	4.5	58.5	72.6	1.1	1,034 (24.1)	1,351 (31.4)	169.5
GMPHD_OGM 38.	Y. Song, K. Yoon, Y. Yoon, K. Yow, M. Jeon. Online Multi-Object Tracking with GMPHD Filter and Occlusion Group Management. In IEEE Access, 2019.
PHD_GSDL 39.	30.5	38.8	28.7	55 (7.6)	297 (41.2)	6,534	35,284	42.6	80.0	28.5	29.5	31.1	68.0	32.6	61.3	75.1	1.1	879 (20.6)	2,208 (51.9)	8.2
PHD_GSDL 39.	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.
MDP 40.	30.3	44.7	32.5	94 (13.0)	277 (38.4)	9,717	32,422	47.2	74.9	34.3	31.3	37.7	67.8	36.2	57.4	75.2	1.7	680 (14.4)	1,500 (31.8)	1.1
MDP 40.	Y. Xiang, A. Alahi, S. Savarese. Learning to Track: Online Multi-Object Tracking by Decision Making. In International Conference on Computer Vision (ICCV), 2015.
Tracker	MOTA	IDF1	HOTA	MT	ML	FP	FN	Rcll	Prcn	AssA	DetA	AssRe	AssPr	DetRe	DetPr	LocA	FAF	ID Sw.	Frag	Hz
MCF_PHD 41.	29.9	38.2	29.8	86 (11.9)	317 (44.0)	8,892	33,529	45.4	75.8	30.0	30.1	32.6	70.6	34.6	57.8	75.7	1.5	656 (14.4)	989 (21.8)	12.2
MCF_PHD 41.	N. Wojke, D. Paulus. Global data association for the Probability Hypothesis Density filter using network flows. In 2016 IEEE International Conference on Robotics and Automation, ICRA, 2016.
UN_DAM 42.	29.7	41.4	0.0	66 (9.2)	360 (49.9)	7,610	35,269	42.6	77.5	0.0	0.0	0.0	0.0	0.0	0.0	100.0	1.3	318 (7.5)	674 (15.8)	20.7
UN_DAM 42.	Multi Object Tracking using Deep Structural Cost Minimization in Data Association
CNNTCM 43.	29.6	36.8	0.0	81 (11.2)	317 (44.0)	7,786	34,733	43.5	77.4	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.3	712 (16.4)	943 (21.7)	1.7
CNNTCM 43.	B. Wang, K. L. Chan, L. Wang, B. Shuai, Z. Zuo, T. Liu, G. Wang. Joint Learning of Convolutional Neural Networks and Temporally Constrained Metrics for Tracklet Association. In DeepVision Workshop (CVPR), 2016.
RSCNN 44.	29.5	37.0	29.6	93 (12.9)	262 (36.3)	11,866	30,474	50.4	72.3	27.2	32.6	30.0	67.1	39.2	56.2	76.7	2.1	976 (19.4)	1,176 (23.3)	4.0
RSCNN 44.	Heba Mahgoub, Khaled Mostafa, Khaled T. Wassif and Ibrahim Farag, “Multi-Target Tracking Using Hierarchical Convolutional Features and Motion Cues” International Journal of Advanced Computer Science and Applications(IJACSA), 8(11), 2017.
TBSS15 45.	29.2	37.2	27.8	49 (6.8)	316 (43.8)	6,068	36,779	40.1	80.3	28.1	27.9	31.2	65.9	30.7	61.3	75.2	1.1	649 (16.2)	1,508 (37.6)	11.5
TBSS15 45.	X. Zhou, P. Jiang, Z. Wei, H. Dong, F. Wang. Online Multi-Object Tracking with Structural Invariance Constraint. In BMVC, 2018.
SCEA 46.	29.1	37.2	28.4	64 (8.9)	341 (47.3)	6,060	36,912	39.9	80.2	29.5	27.7	31.9	70.2	30.5	61.2	75.1	1.0	604 (15.1)	1,182 (29.6)	6.8
SCEA 46.	J. Yoon, C. Lee, M. Yang, K. Yoon. Online Multi-object Tracking via Structural Constraint Event Aggregation. In IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), 2016.
SiameseCNN 47.	29.0	34.3	0.0	61 (8.5)	349 (48.4)	5,160	37,798	38.5	82.1	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.9	639 (16.6)	1,316 (34.2)	52.8
SiameseCNN 47.	Laura Leal-Taixé, Cristian Canton-Ferrer, Konrad Schindler. Learning by Tracking: Siamese CNN for Robust Target Association. DeepVision Workshop (CVPR), Las Vegas (Nevada, USA), June 2016.
HAM_INTP15 48.	28.6	41.4	30.4	72 (10.0)	317 (44.0)	7,485	35,910	41.6	77.3	33.0	28.3	36.2	69.0	31.8	59.1	75.2	1.3	460 (11.1)	1,038 (25.0)	18.7
HAM_INTP15 48.	Y. Yoon, A. Boragule, Y. Song, K. Yoon, M. Jeon. Online Multi-Object Tracking with Historical Appearance Matching and Scene Adaptive Detection Filtering. In IEEE AVSS, 2018.
SLTV15 49.	27.6	40.3	29.3	52 (7.2)	374 (51.9)	6,581	37,566	38.9	78.4	32.6	26.7	35.9	67.7	29.7	59.8	75.4	1.1	358 (9.2)	884 (22.7)	20.9
SLTV15 49.	Gwangju Institute of Science and Technology(GIST), Machine Learning and Vision Laboratory
TBX 50.	27.5	33.8	26.3	75 (10.4)	330 (45.8)	7,968	35,810	41.7	76.3	25.0	28.3	30.1	56.9	31.8	58.2	74.4	1.4	759 (18.2)	1,528 (36.6)	0.1
TBX 50.	R. Henschel, L. Leal-Taixé, B. Rosenhahn, K. Schindler. Tracking with multi-level features. In arXiv:1607.07304, 2016.
Tracker	MOTA	IDF1	HOTA	MT	ML	FP	FN	Rcll	Prcn	AssA	DetA	AssRe	AssPr	DetRe	DetPr	LocA	FAF	ID Sw.	Frag	Hz
GMMA_intp 51.	27.3	36.6	27.6	47 (6.5)	311 (43.1)	7,848	35,817	41.7	76.6	27.2	28.4	31.1	59.4	31.9	58.5	74.6	1.4	987 (23.7)	1,848 (44.3)	132.5
GMMA_intp 51.	Y. Song, Y. Yoon, K. Yoon, M. Jeon. Online and Real-Time Tracking with the GMPHD Filter using Group Management and Relative Motion Analysis. In Proc. IEEE Int. Workshop Traffic Street Surveill. Safety Secur. (AVSS), 2018.
oICF 52.	27.1	40.5	30.1	46 (6.4)	351 (48.7)	7,594	36,757	40.2	76.5	33.1	27.6	39.6	57.7	30.8	58.6	74.0	1.3	454 (11.3)	1,660 (41.3)	1.4
oICF 52.	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.
BiGRU1 53.	26.1	32.2	0.0	47 (6.5)	352 (48.8)	5,761	38,948	36.6	79.6	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	719 (19.6)	2,046 (55.9)	4.0
BiGRU1 53.	Zaifeng Shi, Huizheng Ren, Qingjie Cao, Boyu Fan, and Qiangqiang FanData association framework based on biconnected gated recurrent unit network for multiple object tracking. In JEI, 2020.
TO 54.	25.7	32.7	25.2	31 (4.3)	414 (57.4)	4,779	40,511	34.1	81.4	27.1	24.0	31.6	61.8	26.0	62.2	76.0	0.8	383 (11.2)	600 (17.6)	5.0
TO 54.	S. Manen, R. Timofte, D. Dai, L. Gool. Leveraging single for multi-target tracking using a novel trajectory overlap affinity measure. In 2016 IEEE Winter Conference on Applications of Computer Vision (WACV), 2016.
LP_SSVM 55.	25.2	34.0	26.3	42 (5.8)	382 (53.0)	8,369	36,932	39.9	74.5	26.3	26.7	29.0	66.2	30.4	56.9	75.6	1.4	646 (16.2)	849 (21.3)	41.3
LP_SSVM 55.	S. Wang, C. Fowlkes. Learning Optimal Parameters for Multi-target Tracking with Contextual Interactions. In International Journal of Computer Vision, 2016.
HAM_SADF 56.	25.2	37.8	28.2	41 (5.7)	420 (58.3)	7,330	38,275	37.7	76.0	31.5	25.5	34.2	68.5	28.7	57.8	75.5	1.3	357 (9.5)	745 (19.8)	18.7
HAM_SADF 56.	Y. Yoon, A. Boragule, Y. Song, K. Yoon, M. Jeon. Online Multi-Object Tracking with Historical Appearance Matching and Scene Adaptive Detection Filtering. In IEEE AVSS, 2018.
ELP 57.	25.0	26.2	21.7	54 (7.5)	316 (43.8)	7,345	37,344	39.2	76.6	18.1	26.6	19.2	72.8	29.8	58.3	75.2	1.3	1,396 (35.6)	1,804 (46.0)	5.7
ELP 57.	N. McLaughlin, J. Martinez Del Rincon, P. Miller. Enhancing Linear Programming with Motion Modeling for Multi-target Tracking. In IEEE Winter Conference on Applications of Computer Vision (WACV), 2015.
AdTobKF 58.	24.8	34.5	26.1	29 (4.0)	375 (52.0)	6,201	39,321	36.0	78.1	27.6	25.1	30.2	66.8	27.6	59.8	74.7	1.1	666 (18.5)	1,300 (36.1)	206.5
AdTobKF 58.	K. Loumponias, A. Dimou, N. Vretos, P. Daras. Adaptive Tobit Kalman-Based Tracking. In 2018 14th International Conference on Signal-Image Technology \& Internet-Based Systems (SITIS), 2018.
LINF1 59.	24.5	34.8	26.4	40 (5.5)	466 (64.6)	5,864	40,207	34.6	78.4	29.7	23.7	32.8	66.8	26.1	59.2	75.5	1.0	298 (8.6)	744 (21.5)	7.5
LINF1 59.	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.
TENSOR 60.	24.3	24.1	19.9	40 (5.5)	336 (46.6)	6,644	38,582	37.2	77.5	15.9	25.6	16.9	73.0	28.5	59.3	75.5	1.1	1,271 (34.2)	1,304 (35.1)	24.0
TENSOR 60.	X. Shi, H. Ling, Y. Pang, W. Hu, P. Chu, J. Xing. Rank-1 Tensor Approximation for High-Order Association in Multi-target Tracking. In IJCV, 2019.
Tracker	MOTA	IDF1	HOTA	MT	ML	FP	FN	Rcll	Prcn	AssA	DetA	AssRe	AssPr	DetRe	DetPr	LocA	FAF	ID Sw.	Frag	Hz
TFMOT 61.	23.8	32.3	24.7	35 (4.9)	447 (62.0)	4,533	41,873	31.8	81.2	27.5	22.4	30.1	67.9	24.2	61.7	75.4	0.8	404 (12.7)	792 (24.9)	11.3
TFMOT 61.	M. Abhijeet Boragule. Joint Cost Minimization for Multi-Object Tracking. In 2017 IEEE International Conference on Advanced Vide and Signale Based Surveillance, 2017.
JPDA_m 62.	23.8	33.8	0.0	36 (5.0)	419 (58.1)	6,373	40,084	34.8	77.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.1	365 (10.5)	869 (25.0)	32.6
JPDA_m 62.	H. Rezatofighi, A. Milan, Z. Zhang, Q. Shi, A. Dick, I. Reid. Joint Probabilistic Data Association Revisited. In ICCV, 2015.
MotiCon 63.	23.1	29.4	0.0	34 (4.7)	375 (52.0)	10,404	35,844	41.7	71.1	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.8	1,018 (24.4)	1,061 (25.5)	1.4
MotiCon 63.	L. Leal-Taixé, M. Fenzi, A. Kuznetsova, B. Rosenhahn, S. Savarese. Learning an image-based motion context for multiple people tracking. In CVPR, 2014.
DEEPDA_MOT 64.	22.5	25.9	21.8	46 (6.4)	447 (62.0)	7,346	39,092	36.4	75.3	19.3	25.0	20.8	66.1	27.9	57.8	74.7	1.3	1,159 (31.9)	1,538 (42.3)	172.8
DEEPDA_MOT 64.	K. Yoon, D. Kim, Y. Yoon, M. Jeon. Data Association for Multi-Object Tracking via Deep Neural Networks. In Sensors, 2019.
SegTrack 65.	22.5	31.5	25.1	42 (5.8)	461 (63.9)	7,890	39,020	36.5	74.0	25.8	24.8	29.9	61.5	28.0	56.7	75.4	1.4	697 (19.1)	737 (20.2)	0.2
SegTrack 65.	A. Milan, L. Leal-Taixé, K. Schindler, I. Reid. Joint Tracking and Segmentation of Multiple Targets. In CVPR, 2015.
EAMTTpub 66.	22.3	32.8	25.7	39 (5.4)	380 (52.7)	7,924	38,982	36.6	73.9	26.7	25.2	29.7	65.6	28.2	57.1	74.5	1.4	833 (22.8)	1,485 (40.6)	12.2
EAMTTpub 66.	R. Sanchez-Matilla, F. Poiesi, A. Cavallaro "Multi-target tracking with strong and weak detections" in BMTT ECCVw 2016
SAS_MOT15 67.	22.2	27.2	21.7	22 (3.1)	444 (61.6)	5,591	41,531	32.4	78.1	21.1	22.5	26.3	52.1	24.6	59.3	74.8	1.0	700 (21.6)	1,240 (38.3)	8.9
SAS_MOT15 67.	A. Maksai, P. Fua. Eliminating Exposure Bias and Metric Mismatch in Multiple Object Tracking. In CVPR, 2019.
EDA_GNN 68.	21.8	27.8	23.3	65 (9.0)	290 (40.2)	11,970	34,587	43.7	69.2	19.5	28.4	21.5	61.8	33.6	53.2	74.2	2.1	1,488 (34.0)	1,851 (42.4)	56.4
EDA_GNN 68.	Paper ID 2713
CppSORT 69.	21.7	26.8	21.1	27 (3.7)	354 (49.1)	8,422	38,454	37.4	73.2	18.1	25.1	19.1	72.8	28.6	55.9	75.2	1.5	1,231 (32.9)	2,005 (53.6)	1,112.1
CppSORT 69.	S. Murray. Real-Time Multiple Object Tracking - A Study on the Importance of Speed. In arXiv preprint arXiv:1709.03572, 2017.
OMT_DFH 70.	21.2	37.3	28.1	51 (7.1)	335 (46.5)	13,218	34,657	43.6	67.0	28.4	28.3	34.2	55.6	33.8	52.0	73.5	2.3	563 (12.9)	1,255 (28.8)	28.6
OMT_DFH 70.	J. Ju, D. Kim, B. Ku, D. Han, H. Ko. Online multi-object tracking with efficient track drift and fragmentation handling. In J. Opt. Soc. Am. A, 2017.
Tracker	MOTA	IDF1	HOTA	MT	ML	FP	FN	Rcll	Prcn	AssA	DetA	AssRe	AssPr	DetRe	DetPr	LocA	FAF	ID Sw.	Frag	Hz
MTSTracker 71.	20.6	31.9	0.0	65 (9.0)	266 (36.9)	15,161	32,212	47.6	65.8	0.0	0.0	0.0	0.0	0.0	0.0	0.0	2.6	1,387 (29.2)	2,357 (49.5)	19.3
MTSTracker 71.	F. Nguyen Thi Lan Anh, F. Bremond. Multi-Object Tracking using Multi-Channel Part Appearance Representation. In International conference on Advanced video and Signal Based Surveillance, 2017.
TC_SIAMESE 72.	20.2	32.6	24.6	19 (2.6)	487 (67.5)	6,127	42,596	30.7	75.5	28.7	21.4	31.5	65.6	23.5	57.8	74.9	1.1	294 (9.6)	825 (26.9)	13.0
TC_SIAMESE 72.	Y. Yoon, Y. Song, K. Yoon, M. Jeon. Online Multiple-Object Tracking using Selective Deep Appearance Matching. In IEEE/IEIE The International Conference on Consumer Electronics (ICCE) Asia, 2018.
LP2D 73.	19.8	0.0	0.0	48 (6.7)	297 (41.2)	11,580	36,045	41.3	68.7	0.0	0.0	0.0	0.0	0.0	0.0	0.0	2.0	1,649 (39.9)	1,712 (41.4)	inf
LP2D 73.	MOT baseline: Linear programming on 2D image coordinates.
DCO_X 74.	19.6	31.5	24.7	37 (5.1)	396 (54.9)	10,652	38,232	37.8	68.5	25.0	25.0	29.2	60.7	29.2	53.1	75.0	1.8	521 (13.8)	819 (21.7)	0.3
DCO_X 74.	A. Milan, K. Schindler, S. Roth. Multi-Target Tracking by Discrete-Continuous Energy Minimization. In IEEE PAMI, 2016.
CEM 75.	19.3	0.0	0.0	61 (8.5)	335 (46.5)	14,180	34,591	43.7	65.4	0.0	0.0	0.0	0.0	0.0	0.0	0.0	2.5	813 (18.6)	1,023 (23.4)	1.1
CEM 75.	A. Milan, S. Roth, K. Schindler. Continuous Energy Minimization for Multitarget Tracking. In IEEE TPAMI, 2014.
RNN_LSTM 76.	19.0	17.1	15.8	40 (5.5)	329 (45.6)	11,578	36,706	40.3	68.1	9.7	26.5	23.6	14.7	31.2	52.8	74.3	2.0	1,490 (37.0)	2,081 (51.7)	165.2
RNN_LSTM 76.	A. Milan, S. Rezatofighi, A. Dick, I. Reid, K. Schindler. Online Multi-Target Tracking using Recurrent Neural Networks. In AAAI, 2017.
RMOT 77.	18.6	32.6	25.6	38 (5.3)	384 (53.3)	12,473	36,835	40.0	66.4	25.8	25.8	29.3	58.1	30.7	50.9	73.4	2.2	684 (17.1)	1,282 (32.0)	7.9
RMOT 77.	J. Yoon, H. Yang, J. Lim, K. Yoon. Bayesian Multi-Object Tracking Using Motion Context from Multiple Objects. In IEEE Winter Conference on Applications of Computer Vision (WACV), 2015.
TSDA_OAL 78.	18.6	36.1	28.3	68 (9.4)	305 (42.3)	16,350	32,853	46.5	63.6	27.6	29.5	31.6	58.8	36.5	49.8	73.2	2.8	806 (17.3)	1,544 (33.2)	19.7
TSDA_OAL 78.	H. Ko. Online multi-person tracking with two-stage data association and online appearance model learning. In IET Computer Vision, 2017.
GMPHD 79.	18.5	28.4	21.3	28 (3.9)	399 (55.3)	7,864	41,766	32.0	71.4	21.1	21.7	25.5	53.7	24.5	54.7	74.8	1.4	459 (14.3)	1,266 (39.5)	19.8
GMPHD 79.	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 80.	18.2	0.0	0.0	20 (2.8)	395 (54.8)	8,780	40,310	34.4	70.6	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.5	1,148 (33.4)	2,132 (62.0)	2.7
SMOT 80.	C. Dicle, O. Camps, M. Sznaier. The Way They Move: Tracking Targets with Similar Appearance. In ICCV, 2013.
Tracker	MOTA	IDF1	HOTA	MT	ML	FP	FN	Rcll	Prcn	AssA	DetA	AssRe	AssPr	DetRe	DetPr	LocA	FAF	ID Sw.	Frag	Hz
ALExTRAC 81.	17.0	17.3	0.0	28 (3.9)	378 (52.4)	9,233	39,933	35.0	70.0	0.0	0.0	0.0	0.0	0.0	0.0	100.0	1.6	1,859 (53.1)	1,872 (53.5)	3.7
ALExTRAC 81.	A. Bewley, L. Ott, F. Ramos, B. Upcroft. ALExTRAC: Affinity Learning by Exploring Temporal Reinforcement within Association Chains. In International Conference on Robotics and Automation (ICRA), (to appear) 2016.
TBD 82.	15.9	0.0	0.0	46 (6.4)	345 (47.9)	14,943	34,777	43.4	64.1	0.0	0.0	0.0	0.0	0.0	0.0	0.0	2.6	1,939 (44.7)	1,963 (45.2)	inf
TBD 82.	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.
GSCR 83.	15.8	27.9	21.4	13 (1.8)	440 (61.0)	7,597	43,633	29.0	70.1	22.6	20.5	24.7	66.0	22.7	54.8	73.2	1.3	514 (17.7)	1,010 (34.8)	28.1
GSCR 83.	L. Fagot-Bouquet, R. Audigier, Y. Dhome, F. Lerasle. Online multi-person tracking based on global sparse collaborative representations. In ICIP, 2015.
TC_ODAL 84.	15.1	0.0	0.0	23 (3.2)	402 (55.8)	12,970	38,538	37.3	63.8	0.0	0.0	0.0	0.0	0.0	0.0	0.0	2.2	637 (17.1)	1,716 (46.0)	1.5
TC_ODAL 84.	S. Bae, K. Yoon. Robust Online Multi-Object Tracking based on Tracklet Confidence and Online Discriminative Appearance Learning. In CVPR, 2014.
DP_NMS 85.	14.5	19.7	18.2	43 (6.0)	294 (40.8)	13,171	34,814	43.3	66.9	12.4	27.8	13.1	75.5	33.5	51.7	74.5	2.3	4,537 (104.7)	3,090 (71.3)	444.8
DP_NMS 85.	H. Pirsiavash, D. Ramanan, C. Fowlkes. Globally-Optimal Greedy Algorithms for Tracking a Variable Number of Objects. In CVPR, 2011.
LDCT 86.	4.7	16.8	19.0	82 (11.4)	234 (32.5)	14,066	32,156	47.7	67.6	12.4	30.8	14.5	72.6	37.5	53.1	75.3	2.4	12,348 (259.1)	2,918 (61.2)	20.7
LDCT 86.	F. Solera, S. Calderara, R. Cucchiara. Learning to Divide and Conquer for Online Multi-Target Tracking. In ICCV, 2015

Sequences	Frames	Trajectories	Boxes
11	5783	721	61440

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.
HOTA	higher	100%	Higher Order Tracking Accuracy [3]. Geometric mean of detection accuracy and association accuracy. Averaged across localization thresholds.
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).
Rcll	higher	100%	Ratio of correct detections to total number of GT boxes.
Prcn	higher	100%	Ratio of TP / (TP+FP).
AssA	higher	100%	Association Accuracy [3]. Association Jaccard index averaged over all matching detections and then averaged over localization thresholds.
DetA	higher	100%	Detection Accuracy [3]. Detection Jaccard index averaged over localization thresholds.
AssRe	higher	100%	Association Recall [3]. TPA / (TPA + FNA) averaged over all matching detections and then averaged over localization thresholds.
AssPr	higher	100%	Association Precision [3]. TPA / (TPA + FPA) averaged over all matching detections and then averaged over localization thresholds.
DetRe	higher	100%	Detection Recall [3]. TP /(TP + FN) averaged over localization thresholds.
DetPr	higher	100%	Detection Precision [3]. TP /(TP + FP) averaged over localization thresholds.
LocA	higher	100%	Localization Accuracy [3]. Average localization similarity averaged over all matching detections and averaged over localization thresholds.
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) [4]. 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]	Jonathon Luiten, A.O. & Leibe, B. HOTA: A Higher Order Metric for Evaluating Multi-Object Tracking. International Journal of Computer Vision, 2020.
[4]	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.

MOT15 Results

Download all results for this benchmark

Benchmark Statistics

Evaluation Measures

Legend

References: