2D MOT 2015 Results

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Showing only entries that use public detections!

Tracker	Avg Rank	MOTA	IDF1	MT	ML	FP	FN	ID Sw.	Frag	Hz	Detector
NEW_NW 1.	53.3	-38.8 ±53.1	2.0	0.1%	98.3%	25,606	59,569	80 (26.3)	112 (36.8)	5.8	Public
	Anonymous submission
JPDA_OP 2.	40.6	3.6 ±11.3	7.5	0.4%	96.1%	1,024	58,189	29 (5.5)	119 (22.5)	77.7	Public
	Anonymous submission
TO 3.	40.8	25.7 ±13.5	32.7	4.3%	57.4%	4,779	40,511	383 (11.2)	600 (17.6)	5.0	Public
	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.
SegTrack 4.	48.3	22.5 ±15.2	31.5	5.8%	63.9%	7,890	39,020	697 (19.1)	737 (20.2)	0.2	Public
	A. Milan, L. Leal-Taixé, K. Schindler, I. Reid. Joint Tracking and Segmentation of Multiple Targets. In CVPR, 2015.
LINF1 5.	36.4	24.5 ±15.4	34.8	5.5%	64.6%	5,864	40,207	298 (8.6)	744 (21.5)	7.5	Public
	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.
HAM_SADF 6.	33.1	25.2 ±13.9	37.8	5.7%	58.3%	7,330	38,275	357 (9.5)	745 (19.8)	18.7	Public
	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.
MHT__ReID 7.	26.7	32.6 ±15.1	45.0	14.3%	45.5%	8,101	32,907	376 (8.1)	766 (16.5)	0.3	Public
	Anonymous submission
TFMOT 8.	41.3	23.8 ±11.9	32.3	4.9%	62.0%	4,533	41,873	404 (12.7)	792 (24.9)	11.3	Public
	M. Abhijeet Boragule. Joint Cost Minimization for Multi-Object Tracking. In 2017 IEEE International Conference on Advanced Vide and Signale Based Surveillance, 2017.
DCO_X 9.	45.8	19.6 ±14.1	31.5	5.1%	54.9%	10,652	38,232	521 (13.8)	819 (21.7)	0.3	Public
	A. Milan, K. Schindler, S. Roth. Multi-Target Tracking by Discrete-Continuous Energy Minimization. In IEEE PAMI, 2016.
NOMT 10.	23.1	33.7 ±16.2	44.6	12.2%	44.0%	7,762	32,547	442 (9.4)	823 (17.5)	11.5	Public
	W. Choi. Near-Online Multi-target Tracking with Aggregated Local Flow Descriptor. In ICCV, 2015.
Tracker	Avg Rank	MOTA	IDF1	MT	ML	FP	FN	ID Sw.	Frag	Hz	Detector
TC_SIAMESE 11.	43.9	20.2 ±13.9	32.6	2.6%	67.5%	6,127	42,596	294 (9.6)	825 (26.9)	13.0	Public
	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.
MHT_DAM 12.	26.2	32.4 ±15.6	45.3	16.0%	43.8%	9,064	32,060	435 (9.1)	826 (17.3)	0.7	Public
	C. Kim, F. Li, A. Ciptadi, J. Rehg. Multiple Hypothesis Tracking Revisited. In ICCV, 2015.
LP_SSVM 13.	37.5	25.2 ±13.7	34.0	5.8%	53.0%	8,369	36,932	646 (16.2)	849 (21.3)	41.3	Public
	S. Wang, C. Fowlkes. Learning Optimal Parameters for Multi-target Tracking with Contextual Interactions. In International Journal of Computer Vision, 2016.
JPDA_m 14.	35.8	23.8 ±15.1	33.8	5.0%	58.1%	6,373	40,084	365 (10.5)	869 (25.0)	32.6	Public
	H. Rezatofighi, A. Milan, Z. Zhang, Q. Shi, A. Dick, I. Reid. Joint Probabilistic Data Association Revisited. In ICCV, 2015.
SLTV15 15.	32.3	27.6 ±15.1	40.3	7.2%	51.9%	6,581	37,566	358 (9.2)	884 (22.7)	20.9	Public
	Gwangju Institute of Science and Technology(GIST), Machine Learning and Vision Laboratory
CNNTCM 16.	30.4	29.6 ±13.9	36.8	11.2%	44.0%	7,786	34,733	712 (16.4)	943 (21.7)	1.7	Public
	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.
TSMLCDEnew 17.	25.9	34.3 ±13.1	44.1	14.0%	39.4%	7,869	31,908	618 (12.9)	959 (20.0)	6.5	Public
	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.
TSN 18.	27.0	35.5 ±12.1	43.0	14.4%	43.6%	5,682	33,515	454 (10.0)	967 (21.3)	0.8	Public
	Anonymous submission
JointMC 19.	22.8	35.6 ±18.9	45.1	23.2%	39.3%	10,580	28,508	457 (8.5)	969 (18.1)	0.6	Public
	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.
MCF_PHD 20.	29.7	29.9 ±20.0	38.2	11.9%	44.0%	8,892	33,529	656 (14.4)	989 (21.8)	12.2	Public
	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	Avg Rank	MOTA	IDF1	MT	ML	FP	FN	ID Sw.	Frag	Hz	Detector
GSCR 21.	44.5	15.8 ±10.5	27.9	1.8%	61.0%	7,597	43,633	514 (17.7)	1,010 (34.8)	28.1	Public
	L. Fagot-Bouquet, R. Audigier, Y. Dhome, F. Lerasle. Online multi-person tracking based on global sparse collaborative representations. In ICIP, 2015.
CEM 22.	44.3	19.3 ±17.5	0.0	8.5%	46.5%	14,180	34,591	813 (18.6)	1,023 (23.4)	1.1	Public
	A. Milan, S. Roth, K. Schindler. Continuous Energy Minimization for Multitarget Tracking. In IEEE TPAMI, 2014.
DeepMP 23.	18.6	40.5 ±12.8	28.8	16.8%	35.2%	6,279	29,654	599 (11.6)	1,034 (20.0)	9.6	Public
	Anonymous submission
HAM_INTP15 24.	27.4	28.6 ±13.8	41.4	10.0%	44.0%	7,485	35,910	460 (11.1)	1,038 (25.0)	18.7	Public
	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.
DAC_min 25.	34.2	26.1 ±13.3	36.9	6.8%	50.1%	8,150	36,736	519 (12.9)	1,046 (26.0)	11.6	Public
	GIST
MotiCon 26.	49.2	23.1 ±16.4	29.4	4.7%	52.0%	10,404	35,844	1,018 (24.4)	1,061 (25.5)	1.4	Public
	L. Leal-Taixé, M. Fenzi, A. Kuznetsova, B. Rosenhahn, S. Savarese. Learning an image-based motion context for multiple people tracking. In CVPR, 2014.
siam 27.	34.6	33.0 ±17.0	36.2	8.9%	43.3%	5,101	35,190	853 (20.0)	1,078 (25.2)	1.9	Public
	Anonymous submission
LFNF 28.	32.8	31.6 ±12.3	33.1	9.6%	41.7%	5,943	35,095	961 (22.4)	1,106 (25.8)	4.0	Public
	Sheng H, Hao L, Chen J, et al. Robust Local Effective Matching Model for Multi-Target Tracking. In PCM, 2017
DAC_min 29.	32.8	26.1 ±13.6	37.5	8.9%	48.1%	9,478	35,382	539 (12.7)	1,115 (26.3)	11.6	Public
DSA_MOT 30.	28.8	29.4 ±13.0	38.2	10.0%	45.1%	7,691	35,146	545 (12.7)	1,133 (26.5)	9.6	Public
	Anonymous submission
Tracker	Avg Rank	MOTA	IDF1	MT	ML	FP	FN	ID Sw.	Frag	Hz	Detector
mLK 31.	22.8	35.1 ±12.9	47.5	12.3%	38.3%	5,678	33,815	383 (8.5)	1,175 (26.1)	1.0	Public
	Yuan Zhang, Di Xie and Shiliang Pu (Hikvision Research Institute)
RSCNN 32.	33.2	29.5 ±23.9	37.0	12.9%	36.3%	11,866	30,474	976 (19.4)	1,176 (23.3)	4.0	Public
	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.
SCEA 33.	35.7	29.1 ±12.2	37.2	8.9%	47.3%	6,060	36,912	604 (15.1)	1,182 (29.6)	6.8	Public
	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.
TripBFT15 34.	27.5	37.1 ±15.3	48.4	12.6%	39.7%	8,305	29,732	580 (11.2)	1,193 (23.1)	1.0	Public
	Anonymous submission
SAS_MOT15 35.	48.3	22.2 ±13.8	27.2	3.1%	61.6%	5,591	41,531	700 (21.6)	1,240 (38.3)	8.9	Public
	Submission id 177
OMT_DFH 36.	38.6	21.2 ±17.2	37.3	7.1%	46.5%	13,218	34,657	563 (12.9)	1,255 (28.8)	28.6	Public
	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.
AP_HWDPL_p 37.	18.3	38.5 ±9.9	47.1	8.7%	37.4%	4,005	33,203	586 (12.8)	1,263 (27.5)	6.7	Public
	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.
GMPHD 38.	44.3	18.5 ±12.7	28.4	3.9%	55.3%	7,864	41,766	459 (14.3)	1,266 (39.5)	19.8	Public
	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.
HybridDAT 39.	23.0	35.0 ±15.0	47.7	11.4%	42.2%	8,455	31,140	358 (7.3)	1,267 (25.7)	4.6	Public
	M. Yang, Y. Jia. A Hybrid Data Association Framework for Robust Online Multi-Object Tracking. In IEEE Transactions on Image Processing, 2016.
CRF_RNN15 40.	19.3	38.9 ±15.4	49.3	20.9%	29.4%	10,669	26,291	591 (10.3)	1,270 (22.2)	3.2	Public
	Anonymous submission
Tracker	Avg Rank	MOTA	IDF1	MT	ML	FP	FN	ID Sw.	Frag	Hz	Detector
RMOT 41.	49.9	18.6 ±17.5	32.6	5.3%	53.3%	12,473	36,835	684 (17.1)	1,282 (32.0)	7.9	Public
	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.
TENSOR 42.	46.7	24.3 ±13.2	24.1	5.5%	46.6%	6,644	38,582	1,271 (34.2)	1,304 (35.1)	24.0	Public
	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.
SST_MOT15 43.	31.8	35.8 ±19.3	39.6	7.8%	39.0%	4,065	33,669	1,728 (38.2)	1,312 (29.0)	6.3	Public
	Shijie Sun, Naveed Akhtar, Ajmal Mian
SiameseCNN 44.	35.3	29.0 ±15.1	34.3	8.5%	48.4%	5,160	37,798	639 (16.6)	1,316 (34.2)	52.8	Public
	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.
QuadMOT 45.	28.4	33.8 ±14.8	40.4	12.9%	36.9%	7,898	32,061	703 (14.7)	1,430 (29.9)	3.7	Public
	J. Son, M. Baek, M. Cho, B. Han. Multi-Object Tracking with Quadruplet Convolutional Neural Networks. In CVPR, 2017.
KCF 46.	23.4	38.9 ±14.5	44.5	16.6%	31.5%	7,321	29,501	720 (13.9)	1,440 (27.7)	0.3	Public
	P. Chu, H. Fan, C. Tan, H. Ling. Online Multi-Object Tracking with Instance-Aware Tracker and Dynamic Model Refreshment. In WACV, 2019.
AM 47.	22.2	34.3 ±13.7	48.3	11.4%	43.4%	5,154	34,848	348 (8.0)	1,463 (33.8)	0.5	Public
	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.
cf_mdp 48.	20.3	31.4 ±14.7	43.7	16.1%	34.3%	10,118	31,284	718 (14.6)	1,469 (29.9)	450.4	Public
	Anonymous submission
EAMTTpub 49.	44.2	22.3 ±14.2	32.8	5.4%	52.7%	7,924	38,982	833 (22.8)	1,485 (40.6)	12.2	Public
	R. Sanchez-Matilla, F. Poiesi, A. Cavallaro "Multi-target tracking with strong and weak detections" in BMTT ECCVw 2016
MDP 50.	31.3	30.3 ±14.6	44.7	13.0%	38.4%	9,717	32,422	680 (14.4)	1,500 (31.8)	1.1	Public
	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	Avg Rank	MOTA	IDF1	MT	ML	FP	FN	ID Sw.	Frag	Hz	Detector
TDAM 51.	28.5	33.0 ±9.8	46.1	13.3%	39.1%	10,064	30,617	464 (9.2)	1,506 (30.0)	5.9	Public
	M. Yang, Y. Jia. Temporal dynamic appearance modeling for online multi-person tracking. In Computer Vision and Image Understanding, 2016.
TBSS15 52.	37.7	29.2 ±12.5	37.2	6.8%	43.8%	6,068	36,779	649 (16.2)	1,508 (37.6)	11.5	Public
	X. Zhou, P. Jiang, Z. Wei, H. Dong, F. Wang. Online Multi-Object Tracking with Structural Invariance Constraint. In BMVC, 2018.
RAR15pub 53.	25.3	35.1 ±12.5	45.4	13.0%	42.3%	6,771	32,717	381 (8.1)	1,523 (32.6)	5.4	Public
	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.
TBX 54.	44.5	27.5 ±13.3	33.8	10.4%	45.8%	7,968	35,810	759 (18.2)	1,528 (36.6)	0.1	Public
	R. Henschel, L. Leal-Taixé, B. Rosenhahn, K. Schindler. Tracking with multi-level features. In arXiv:1607.07304, 2016.
DEEPDA_MOT 55.	43.9	22.5 ±17.7	25.9	6.4%	62.0%	7,346	39,092	1,159 (31.9)	1,538 (42.3)	172.8	Public
	K. Yoon, D. Kim, Y. Yoon, M. Jeon. Data Association for Multi-Object Tracking via Deep Neural Networks. In Sensors, 2019.
TSDA_OAL 56.	43.8	18.6 ±17.6	36.1	9.4%	42.3%	16,350	32,853	806 (17.3)	1,544 (33.2)	19.7	Public
	H. Ko. Online multi-person tracking with two-stage data association and online appearance model learning. In IET Computer Vision, 2017.
DCCRF 57.	29.9	33.6 ±11.0	39.1	10.4%	37.6%	5,917	34,002	866 (19.4)	1,566 (35.1)	0.1	Public
	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.
CDA_DDALpb 58.	28.1	32.8 ±10.6	38.8	9.7%	42.2%	4,983	35,690	614 (14.7)	1,583 (37.8)	2.3	Public
	S. Bae and K. Yoon, Confidence-Based Data Association and Discriminative Deep Appearance Learning for Robust Online Multi-Object Tracking , In IEEE TPAMI, 2017.
CMOT 59.	30.3	27.6 ±14.0	43.3	10.5%	49.0%	9,451	34,207	800 (18.0)	1,584 (35.7)	5,783.0	Public
	Anonymous submission
TripT15 60.	32.1	35.7 ±14.9	47.7	11.1%	39.8%	8,729	30,152	655 (12.9)	1,614 (31.7)	1.1	Public
	Anonymous submission
Tracker	Avg Rank	MOTA	IDF1	MT	ML	FP	FN	ID Sw.	Frag	Hz	Detector
oICF 61.	39.8	27.1 ±14.9	40.5	6.4%	48.7%	7,594	36,757	454 (11.3)	1,660 (41.3)	1.4	Public
	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.
DCOR 62.	43.3	22.4 ±12.1	24.7	3.3%	57.4%	5,603	41,410	634 (19.4)	1,686 (51.7)	37.6	Public
	Anonymous submission
CF_MCMC 63.	33.0	31.4 ±11.3	36.4	10.3%	40.9%	8,798	32,541	814 (17.3)	1,711 (36.4)	3.2	Public
	Anonymous submission
LP2D 64.	47.3	19.8 ±14.2	0.0	6.7%	41.2%	11,580	36,045	1,649 (39.9)	1,712 (41.4)	112.1	Public
	MOT baseline: Linear programming on 2D image coordinates.
TC_ODAL 65.	60.1	15.1 ±15.0	0.0	3.2%	55.8%	12,970	38,538	637 (17.1)	1,716 (46.0)	1.7	Public
	S. Bae, K. Yoon. Robust Online Multi-Object Tracking based on Tracklet Confidence and Online Discriminative Appearance Learning. In CVPR, 2014.
ELP 66.	43.8	25.0 ±10.8	26.2	7.5%	43.8%	7,345	37,344	1,396 (35.6)	1,804 (46.0)	5.7	Public
	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.
YT 67.	49.2	23.5 ±11.6	26.8	4.4%	49.5%	5,537	40,210	1,270 (36.8)	1,817 (52.6)	34.4	Public
	Anonymous submission
EDA_GNN 68.	40.8	21.8 ±13.8	27.8	9.0%	40.2%	11,970	34,587	1,488 (34.0)	1,851 (42.4)	56.4	Public
	Paper ID 2713
ALExTRAC 69.	54.3	17.0 ±12.1	17.3	3.9%	52.4%	9,233	39,933	1,859 (53.1)	1,872 (53.5)	3.7	Public
	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 70.	59.3	15.9 ±17.6	0.0	6.4%	47.9%	14,943	34,777	1,939 (44.7)	1,963 (45.2)	0.7	Public
	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.
Tracker	Avg Rank	MOTA	IDF1	MT	ML	FP	FN	ID Sw.	Frag	Hz	Detector
CppSORT 71.	45.6	21.7 ±11.8	26.8	3.7%	49.1%	8,422	38,454	1,231 (32.9)	2,005 (53.6)	1,112.1	Public
	S. Murray. Real-Time Multiple Object Tracking - A Study on the Importance of Speed. In arXiv preprint arXiv:1709.03572, 2017.
AMIR15 72.	23.3	37.6 ±12.5	46.0	15.8%	26.8%	7,933	29,397	1,026 (19.7)	2,024 (38.8)	1.9	Public
	A. Sadeghian, A. Alahi, S. Savarese. Tracking The Untrackable: Learning To Track Multiple Cues with Long-Term Dependencies. In ICCV, 2017.
RNN_LSTM 73.	51.5	19.0 ±15.2	17.1	5.5%	45.6%	11,578	36,706	1,490 (37.0)	2,081 (51.7)	165.2	Public
	A. Milan, S. Rezatofighi, A. Dick, I. Reid, K. Schindler. Online Multi-Target Tracking using Recurrent Neural Networks. In AAAI, 2017.
SMOT 74.	60.9	18.2 ±10.3	0.0	2.8%	54.8%	8,780	40,310	1,148 (33.4)	2,132 (62.0)	2.7	Public
	C. Dicle, O. Camps, M. Sznaier. The Way They Move: Tracking Targets with Similar Appearance. In ICCV, 2013.
PHD_GSDL 75.	36.9	30.5 ±14.9	38.8	7.6%	41.2%	6,534	35,284	879 (20.6)	2,208 (51.9)	8.2	Public
	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.
ZRT 76.	43.9	22.8 ±19.3	37.8	9.7%	38.4%	14,423	31,932	1,080 (22.5)	2,267 (47.2)	5.3	Public
	Anonymous submission
RETR15 77.	43.0	20.8 ±14.5	37.4	9.2%	38.6%	15,501	31,988	1,172 (24.4)	2,325 (48.5)	7.2	Public
	Anonymous submission
MTSTracker 78.	42.9	20.6 ±18.2	31.9	9.0%	36.9%	15,161	32,212	1,387 (29.2)	2,357 (49.5)	19.3	Public
	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.
SNM 79.	33.6	31.3 ±16.5	38.2	12.6%	35.4%	8,903	32,393	926 (19.6)	2,382 (50.4)	14.8	Public
	Anonymous submission
MCFCOS_CNN 80.	44.0	25.5 ±20.8	32.0	9.3%	34.4%	12,344	31,378	2,064 (42.2)	2,618 (53.5)	0.5	Public
	Anonymous submission
Tracker	Avg Rank	MOTA	IDF1	MT	ML	FP	FN	ID Sw.	Frag	Hz	Detector
KCF_Simple 81.	53.6	18.3 ±11.1	25.1	2.6%	49.8%	8,976	39,805	1,436 (40.8)	2,634 (74.8)	35.6	Public
	Anonymous submission
HSJ_Sia 82.	48.3	20.9 ±13.0	29.2	4.0%	51.6%	6,457	40,477	1,695 (49.7)	2,734 (80.1)	70.3	Public
	Anonymous submission
LDCT 83.	44.4	4.7 ±41.3	16.8	11.4%	32.5%	14,066	32,156	12,348 (259.1)	2,918 (61.2)	20.7	Public
	F. Solera, S. Calderara, R. Cucchiara. Learning to Divide and Conquer for Online Multi-Target Tracking. In ICCV, 2015
DS_RNN 84.	38.8	27.8 ±11.4	29.6	7.1%	37.9%	6,903	35,238	2,192 (51.4)	3,011 (70.6)	19.3	Public
	Anonymous submission
DP_NMS 85.	47.5	14.5 ±14.5	19.7	6.0%	40.8%	13,171	34,814	4,537 (104.7)	3,090 (71.3)	444.8	Public
	H. Pirsiavash, D. Ramanan, C. Fowlkes. Globally-Optimal Greedy Algorithms for Tracking a Variable Number of Objects. In CVPR, 2011.
HOHOTRACK 86.	33.3	26.8 ±21.7	32.9	28.6%	16.9%	18,994	24,549	1,411 (23.5)	3,417 (56.9)	26.7	Public
	Anonymous submission
EFMC 87.	52.3	14.9 ±9.8	11.5	3.3%	50.5%	5,583	41,085	5,623 (169.7)	3,443 (103.9)	24.6	Public
	Anonymous submission
Bar_dist2 88.	42.2	20.1 ±14.5	21.7	7.4%	36.8%	11,161	34,874	3,084 (71.3)	3,823 (88.4)	215.8	Public
	Anonymous submission
Bar_dist 89.	44.3	18.0 ±16.0	17.6	5.7%	35.9%	10,019	36,222	4,161 (101.4)	5,043 (122.9)	2,891.5	Public
	Anonymous submission

Difficulty Analysis

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

TUD-Crossing

(60.8% MOTA)

PETS09-S2L2

(40.5% MOTA)

ETH-Jelmoli

(36.4% MOTA)

...

...

Venice-1

(20.3% MOTA)

ADL-Rundle-1

(16.1% MOTA)