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.
End_To_End 12.	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 12.	A. Boragule, H. Jang, N. Ha, M. Jeon. Pixel-Guided Association for Multi-Object Tracking. In Sensors, 2022.
MOMOT_GLMB 13.	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 13.	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_ 14.	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_ 14.	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 15.	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 15.	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 16.	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 16.	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 17.	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 17.	J. Xu, Y. Cao, Z. Zhang, H. Hu. Spatial-Temporal Relation Networks for Multi-Object Tracking. In ICCV, 2019.
AMIR15 18.	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 18.	A. Sadeghian, A. Alahi, S. Savarese. Tracking The Untrackable: Learning To Track Multiple Cues with Long-Term Dependencies. In ICCV, 2017.
TPM 19.	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 19.	J. Peng, T. Wang, et.al. TPM: Multiple Object Tracking with Tracklet-Plane Matching. In Pattern Recognition, 2020.
SST_MOT15 20.	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 20.	Shijie Sun, Naveed Akhtar, Ajmal Mian
Tracker	MOTA	IDF1	HOTA	MT	ML	FP	FN	Rcll	Prcn	AssA	DetA	AssRe	AssPr	DetRe	DetPr	LocA	FAF	ID Sw.	Frag	Hz
JointMC 21.	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 21.	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 22.	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 22.	J. Peng, F. Qiu, et.al. Tracklet Siamese Network with Constrained Clustering for Multiple Object Tracking. In VCIP, 2018.
RAR15pub 23.	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 23.	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 24.	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 24.	Yuan Zhang, Di Xie and Shiliang Pu (Hikvision Research Institute)
HybridDAT 25.	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 25.	M. Yang, Y. Jia. A Hybrid Data Association Framework for Robust Online Multi-Object Tracking. In IEEE Transactions on Image Processing, 2016.
INARLA 26.	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 26.	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 27.	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 27.	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 28.	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 28.	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 29.	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 29.	J. Son, M. Baek, M. Cho, B. Han. Multi-Object Tracking with Quadruplet Convolutional Neural Networks. In CVPR, 2017.
NOMT 30.	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 30.	W. Choi. Near-Online Multi-target Tracking with Aggregated Local Flow Descriptor. In ICCV, 2015.
Tracker	MOTA	IDF1	HOTA	MT	ML	FP	FN	Rcll	Prcn	AssA	DetA	AssRe	AssPr	DetRe	DetPr	LocA	FAF	ID Sw.	Frag	Hz
DCCRF 31.	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 31.	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 32.	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 32.	M. Yang, Y. Jia. Temporal dynamic appearance modeling for online multi-person tracking. In Computer Vision and Image Understanding, 2016.
CDA_DDALpb 33.	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 33.	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 34.	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 34.	C. Kim, F. Li, A. Ciptadi, J. Rehg. Multiple Hypothesis Tracking Revisited. In ICCV, 2015.
LFNF 35.	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 35.	Sheng H, Hao L, Chen J, et al. Robust Local Effective Matching Model for Multi-Target Tracking. In PCM, 2017
EDR15 36.	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 36.	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 37.	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 37.	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 38.	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 38.	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 39.	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 39.	Y. Xiang, A. Alahi, S. Savarese. Learning to Track: Online Multi-Object Tracking by Decision Making. In International Conference on Computer Vision (ICCV), 2015.
MCF_PHD 40.	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 40.	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.
Tracker	MOTA	IDF1	HOTA	MT	ML	FP	FN	Rcll	Prcn	AssA	DetA	AssRe	AssPr	DetRe	DetPr	LocA	FAF	ID Sw.	Frag	Hz
UN_DAM 41.	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 41.	Multi Object Tracking using Deep Structural Cost Minimization in Data Association
CNNTCM 42.	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 42.	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 43.	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 43.	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 44.	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 44.	X. Zhou, P. Jiang, Z. Wei, H. Dong, F. Wang. Online Multi-Object Tracking with Structural Invariance Constraint. In BMVC, 2018.
SCEA 45.	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 45.	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 46.	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 46.	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 47.	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 47.	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 48.	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 48.	Gwangju Institute of Science and Technology(GIST), Machine Learning and Vision Laboratory
TBX 49.	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 49.	R. Henschel, L. Leal-Taixé, B. Rosenhahn, K. Schindler. Tracking with multi-level features. In arXiv:1607.07304, 2016.
GMMA_intp 50.	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 50.	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.
Tracker	MOTA	IDF1	HOTA	MT	ML	FP	FN	Rcll	Prcn	AssA	DetA	AssRe	AssPr	DetRe	DetPr	LocA	FAF	ID Sw.	Frag	Hz
oICF 51.	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 51.	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 52.	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 52.	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 53.	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 53.	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 54.	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 54.	S. Wang, C. Fowlkes. Learning Optimal Parameters for Multi-target Tracking with Contextual Interactions. In International Journal of Computer Vision, 2016.
HAM_SADF 55.	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 55.	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 56.	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 56.	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 57.	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 57.	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 58.	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 58.	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 59.	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 59.	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.
TFMOT 60.	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 60.	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	HOTA	MT	ML	FP	FN	Rcll	Prcn	AssA	DetA	AssRe	AssPr	DetRe	DetPr	LocA	FAF	ID Sw.	Frag	Hz
JPDA_m 61.	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 61.	H. Rezatofighi, A. Milan, Z. Zhang, Q. Shi, A. Dick, I. Reid. Joint Probabilistic Data Association Revisited. In ICCV, 2015.
MotiCon 62.	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 62.	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 63.	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 63.	K. Yoon, D. Kim, Y. Yoon, M. Jeon. Data Association for Multi-Object Tracking via Deep Neural Networks. In Sensors, 2019.
SegTrack 64.	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 64.	A. Milan, L. Leal-Taixé, K. Schindler, I. Reid. Joint Tracking and Segmentation of Multiple Targets. In CVPR, 2015.
EAMTTpub 65.	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 65.	R. Sanchez-Matilla, F. Poiesi, A. Cavallaro "Multi-target tracking with strong and weak detections" in BMTT ECCVw 2016
SAS_MOT15 66.	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 66.	A. Maksai, P. Fua. Eliminating Exposure Bias and Metric Mismatch in Multiple Object Tracking. In CVPR, 2019.
EDA_GNN 67.	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 67.	Paper ID 2713
CppSORT 68.	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 68.	S. Murray. Real-Time Multiple Object Tracking - A Study on the Importance of Speed. In arXiv preprint arXiv:1709.03572, 2017.
OMT_DFH 69.	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 69.	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.
MTSTracker 70.	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 70.	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.
Tracker	MOTA	IDF1	HOTA	MT	ML	FP	FN	Rcll	Prcn	AssA	DetA	AssRe	AssPr	DetRe	DetPr	LocA	FAF	ID Sw.	Frag	Hz
TC_SIAMESE 71.	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 71.	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 72.	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 72.	MOT baseline: Linear programming on 2D image coordinates.
DCO_X 73.	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 73.	A. Milan, K. Schindler, S. Roth. Multi-Target Tracking by Discrete-Continuous Energy Minimization. In IEEE PAMI, 2016.
CEM 74.	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 74.	A. Milan, S. Roth, K. Schindler. Continuous Energy Minimization for Multitarget Tracking. In IEEE TPAMI, 2014.
RNN_LSTM 75.	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 75.	A. Milan, S. Rezatofighi, A. Dick, I. Reid, K. Schindler. Online Multi-Target Tracking using Recurrent Neural Networks. In AAAI, 2017.
RMOT 76.	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 76.	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 77.	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 77.	H. Ko. Online multi-person tracking with two-stage data association and online appearance model learning. In IET Computer Vision, 2017.
GMPHD 78.	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 78.	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 79.	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 79.	C. Dicle, O. Camps, M. Sznaier. The Way They Move: Tracking Targets with Similar Appearance. In ICCV, 2013.
ALExTRAC 80.	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 80.	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.
Tracker	MOTA	IDF1	HOTA	MT	ML	FP	FN	Rcll	Prcn	AssA	DetA	AssRe	AssPr	DetRe	DetPr	LocA	FAF	ID Sw.	Frag	Hz
TBD 81.	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 81.	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 82.	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 82.	L. Fagot-Bouquet, R. Audigier, Y. Dhome, F. Lerasle. Online multi-person tracking based on global sparse collaborative representations. In ICIP, 2015.
TC_ODAL 83.	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 83.	S. Bae, K. Yoon. Robust Online Multi-Object Tracking based on Tracklet Confidence and Online Discriminative Appearance Learning. In CVPR, 2014.
DP_NMS 84.	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 84.	H. Pirsiavash, D. Ramanan, C. Fowlkes. Globally-Optimal Greedy Algorithms for Tracking a Variable Number of Objects. In CVPR, 2011.
LDCT 85.	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 85.	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: