2D MOT 2015 Results

Download all results for this benchmark

Click on a measure to sort the table accordingly. See below for a more detailed description.

Showing only entries that use public detections!

Tracker	Avg Rank	MOTA	IDF1	MT	ML	FP	FN	ID Sw.	Frag	Hz	Detector
TO 1.	34.4	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 2.	41.1	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 3.	30.4	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 4.	27.7	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.
TFMOT 5.	34.8	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 6.	39.7	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 7.	18.4	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.
TC_SIAMESE 8.	37.0	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 9.	21.0	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.
CNNEMBED 10.	25.2	23.6 ±14.9	41.0	12.9%	44.1%	14,047	32,398	496 (10.5)	829 (17.5)	1.9	Public
	Anonymous submission
Tracker	Avg Rank	MOTA	IDF1	MT	ML	FP	FN	ID Sw.	Frag	Hz	Detector
LP_SSVM 11.	31.6	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 12.	30.3	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 13.	27.3	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
TPM 14.	20.7	36.2 ±12.8	43.6	15.4%	42.6%	5,650	33,102	420 (9.1)	928 (20.1)	0.8	Public
	Anonymous submission
CNNTCM 15.	26.5	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 16.	20.7	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 17.	22.9	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 18.	19.0	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, Z. Yu, B. Andres, T. Brox, B. Schiele. A Multi-cut Formulation for Joint Segmentation and Tracking of Multiple Objects. In CoRR, 2016.
MCF_PHD 19.	25.0	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.
GSCR 20.	37.8	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.
Tracker	Avg Rank	MOTA	IDF1	MT	ML	FP	FN	ID Sw.	Frag	Hz	Detector
CEM 21.	38.0	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.
HAM_INTP15 22.	23.3	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 23.	29.4	26.1 ±13.3	36.9	6.8%	50.1%	8,150	36,736	519 (12.9)	1,046 (26.0)	11.6	Public
	Deep Appearance & Contextual Minimization for Multi-Object Tracking
MotiCon 24.	41.9	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.
LFNF 25.	27.8	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 26.	28.3	26.3 ±13.4	37.7	8.7%	47.4%	9,608	35,146	548 (12.8)	1,123 (26.2)	11.6	Public
mLK 27.	18.5	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 28.	27.3	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 29.	30.4	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 30.	22.4	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
Tracker	Avg Rank	MOTA	IDF1	MT	ML	FP	FN	ID Sw.	Frag	Hz	Detector
SAS_MOT15 31.	40.8	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 32.	32.6	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 33.	15.3	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 34.	37.4	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 35.	18.4	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.
RMOT 36.	42.3	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.
SiameseCNN 37.	30.1	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 38.	23.0	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.
AM 39.	18.8	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.
EAMTTpub 40.	37.3	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
Tracker	Avg Rank	MOTA	IDF1	MT	ML	FP	FN	ID Sw.	Frag	Hz	Detector
MDP 41.	25.6	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.
TDAM 42.	22.9	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 43.	31.9	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 44.	20.1	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 45.	37.9	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.
TSDA_OAL 46.	37.2	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 47.	26.0	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 48.	23.8	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 49.	26.2	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 50.	26.4	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 51.	33.4	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.
CF_MCMC 52.	27.9	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 53.	39.3	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 54.	50.7	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 55.	36.7	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 56.	41.8	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
ALExTRAC 57.	45.5	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 58.	50.2	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.
CppSORT 59.	39.3	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 60.	18.5	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.
Tracker	Avg Rank	MOTA	IDF1	MT	ML	FP	FN	ID Sw.	Frag	Hz	Detector
RNN_LSTM 61.	43.3	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 62.	51.2	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 63.	30.6	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 64.	36.9	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 65.	35.8	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 66.	35.8	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 67.	27.5	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
Otakudj 68.	31.4	29.2 ±13.9	42.2	12.3%	33.8%	9,558	32,008	1,924 (40.2)	2,463 (51.4)	0.6	Public
	Anonymous submission
ARMS 69.	26.6	30.1 ±13.4	43.3	11.8%	33.8%	9,117	32,042	1,785 (37.3)	2,483 (51.9)	1.7	Public
	Anonymous submission
LDCT 70.	36.7	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
Tracker	Avg Rank	MOTA	IDF1	MT	ML	FP	FN	ID Sw.	Frag	Hz	Detector
DP_NMS 71.	40.0	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.
EFMC 72.	43.9	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
siam 73.	46.8	8.2 ±20.5	20.3	5.4%	37.3%	20,088	33,402	2,900 (63.5)	3,443 (75.4)	1.9	Public
	Anonymous submission
Bar_dist2 74.	35.5	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 75.	37.4	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

(62.7% MOTA)

PETS09-S2L2

(41.4% MOTA)

ETH-Jelmoli

(38.3% MOTA)

...

...

Venice-1

(20.8% MOTA)

ADL-Rundle-1

(16.3% MOTA)