MOT16 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
DS_v2 1.	19.1	59.3 ±12.9	57.5	24.2%	29.1%	7,465	65,810	887 (13.9)	2,738 (42.8)	39.4	Public
	Anonymous submission
MTT_TPR 2.	24.9	54.9 ±11.7	53.1	18.7%	34.8%	4,130	76,673	1,447 (25.0)	3,693 (63.7)	6.7	Public
	Anonymous submission
HDTR 3.	18.3	53.6 ±8.7	46.6	21.2%	37.0%	4,714	79,353	618 (10.9)	833 (14.7)	3.6	Public
TPM 4.	24.3	51.3 ±9.3	47.9	18.7%	40.8%	2,701	85,504	569 (10.7)	707 (13.3)	0.8	Public
	Anonymous submission
PV 5.	31.2	50.4 ±10.1	50.8	14.9%	38.9%	2,600	86,780	1,061 (20.2)	3,181 (60.7)	7.3	Public
	Anonymous submission
MEN 6.	27.2	50.0 ±9.1	52.8	15.0%	37.0%	6,117	84,271	706 (13.1)	1,797 (33.4)	2.0	Public
	Anonymous submission
RTT 7.	35.9	50.0 ±8.0	39.5	17.3%	34.0%	7,061	82,900	1,152 (21.1)	2,237 (41.0)	1.8	Public
	Anonymous submission
NOTA 8.	22.0	49.8 ±8.3	55.3	17.9%	37.7%	7,248	83,614	614 (11.3)	1,372 (25.3)	19.2	Public
	Anonymous submission
PDetTracId 9.	33.6	49.7 ±9.4	46.8	16.7%	37.3%	4,393	86,241	1,040 (19.7)	3,652 (69.3)	2.4	Public
	Anonymous submission
TAR 10.	35.7	49.4 ±8.1	40.0	18.4%	30.6%	11,220	79,839	1,180 (21.0)	2,052 (36.5)	5.0	Public
	Anonymous submission
Tracker	Avg Rank	MOTA	IDF1	MT	ML	FP	FN	ID Sw.	Frag	Hz	Detector
siameseCos 11.	26.9	49.4 ±8.4	49.8	19.1%	39.4%	6,281	85,384	679 (12.8)	823 (15.5)	0.8	Public
	In preparation
STCG 12.	23.1	49.3 ±8.6	52.0	16.2%	41.4%	6,886	84,979	515 (9.6)	775 (14.5)	22.3	Public
	Anonymous submission
HCC 13.	21.2	49.3 ±10.2	50.7	17.8%	39.9%	5,333	86,795	391 (7.5)	535 (10.2)	0.8	Public
	L. Ma, S. Tang, M. Black, L. Gool. Customized Multi-Person Tracker. In Computer Vision -- ACCV 2018, 2018.
LSST16O 14.	31.8	49.2 ±10.2	56.5	13.4%	41.4%	7,187	84,875	606 (11.3)	2,497 (46.7)	2.0	Public
	Anonymous submission
eTC 15.	24.8	49.2 ±9.1	56.1	17.3%	40.3%	8,400	83,702	606 (11.2)	882 (16.3)	0.7	Public
	G. Wang, Y. Wang, H. Zhang, R. Gu, J. Hwang. Exploit the Connectivity: Multi-Object Tracking with TrackletNet. In arXiv preprint arXiv:1811.07258, 2018.
AFN 16.	27.3	49.0 ±10.2	48.2	19.1%	35.7%	9,508	82,506	899 (16.4)	1,383 (25.3)	0.6	Public
	H. Shen, L. Huang, C. Huang, W. Xu. Tracklet Association Tracker: An End-to-End Learning-based Association Approach for Multi-Object Tracking. In CoRR, 2018.
CRF_RNN16 17.	23.1	49.0 ±7.2	53.9	18.1%	35.8%	8,495	83,838	621 (11.5)	1,252 (23.2)	1.3	Public
	Anonymous submission
KCF16 18.	33.2	48.8 ±9.6	47.2	15.8%	38.1%	5,875	86,567	906 (17.3)	1,116 (21.2)	0.1	Public
	P. Chu, H. Fan, C. Tan, H. Ling. Online Multi-Object Tracking with Instance-Aware Tracker and Dynamic Model Refreshment. In WACV, 2019.
LMP 19.	24.8	48.8 ±9.8	51.3	18.2%	40.1%	6,654	86,245	481 (9.1)	595 (11.3)	0.5	Public
	S. Tang, M. Andriluka, B. Andres, B. Schiele. Multiple People Tracking with Lifted Multicut and Person Re-identification. In CVPR, 2017.
DeepMP16 20.	21.9	48.7 ±10.3	50.1	15.0%	43.6%	4,111	88,862	535 (10.4)	873 (17.0)	9.9	Public
	Anonymous submission
Tracker	Avg Rank	MOTA	IDF1	MT	ML	FP	FN	ID Sw.	Frag	Hz	Detector
TLMHT 21.	27.0	48.7 ±8.6	55.3	15.7%	44.5%	6,632	86,504	413 (7.9)	642 (12.2)	4.8	Public
	H. Sheng, J. Chen, Y. Zhang, W. Ke, Z. Xiong, J. Yu. Iterative Multiple Hypothesis Tracking with Tracklet-level Association. In IEEE Transactions on Circuits and Systems for Video Technology, 2018.
TripBFT 22.	30.7	48.3 ±8.1	50.9	15.4%	40.1%	2,706	91,047	543 (10.8)	896 (17.9)	0.5	Public
	Anonymous submission
AOReid 23.	26.4	48.2 ±8.7	50.8	15.3%	36.8%	10,283	83,301	821 (15.1)	1,963 (36.1)	11.2	Public
	Anonymous submission
TSN 24.	33.3	48.2 ±8.7	45.7	19.9%	38.9%	8,447	85,315	665 (12.5)	829 (15.6)	0.8	Public
	Anonymous submission
DAST 25.	29.5	48.2 ±8.3	50.7	14.1%	37.2%	8,869	84,784	838 (15.7)	2,028 (37.9)	8.7	Public
	Anonymous submission
GCRA 26.	31.1	48.2 ±8.3	48.6	12.9%	41.1%	5,104	88,586	821 (16.0)	1,117 (21.7)	2.8	Public
	C. Ma, C. Yang, F. Yang, Y. Zhuang, Z. Zhang, H. Jia, X. Xie. Trajectory Factory: Tracklet Cleaving and Re-connection by Deep Siamese Bi-GRU for Multiple Object Tracking. In ICME, 2018.
SRPN16 27.	36.1	48.2 ±8.5	51.3	14.2%	36.8%	7,767	85,973	790 (14.9)	2,006 (38.0)	1.4	Public
	Anonymous submission
TripT 28.	31.5	48.1 ±8.5	51.9	15.8%	40.2%	2,827	91,210	563 (11.3)	1,143 (22.9)	0.6	Public
	Anonymous submission
CMT16 29.	21.4	48.1 ±9.0	56.6	15.7%	46.2%	7,758	86,501	381 (7.2)	615 (11.7)	6.3	Public
	Anonymous submission
FWT 30.	34.3	47.8 ±9.4	44.3	19.1%	38.2%	8,886	85,487	852 (16.0)	1,534 (28.9)	0.6	Public
	R. Henschel, L. Leal-Taixé, D. Cremers, B. Rosenhahn. Fusion of Head and Full-Body Detectors for Multi-Object Tracking. In Trajnet CVPRW, 2018.
Tracker	Avg Rank	MOTA	IDF1	MT	ML	FP	FN	ID Sw.	Frag	Hz	Detector
MOTDT 31.	31.9	47.6 ±8.2	50.9	15.2%	38.3%	9,253	85,431	792 (14.9)	1,858 (35.0)	20.6	Public
	C. Long, A. Haizhou, Z. Zijie, S. Chong. Real-time Multiple People Tracking with Deeply Learned Candidate Selection and Person Re-identification. In ICME, 2018.
NLLMPa 32.	27.1	47.6 ±10.6	47.3	17.0%	40.4%	5,844	89,093	629 (12.3)	768 (15.0)	8.3	Public
	E. Levinkov, J. Uhrig, S. Tang, M. Omran, E. Insafutdinov, A. Kirillov, C. Rother, T. Brox, B. Schiele, B. Andres. Joint Graph Decomposition and Node Labeling: Problem, Algorithms, Applications. In CVPR, 2017.
EAGS16 33.	24.7	47.4 ±10.4	50.1	17.3%	42.7%	8,369	86,931	575 (11.0)	913 (17.5)	197.3	Public
	H. Sheng, X. Zhang, Y. Zhang, Y. Wu, J. Chen. Enhanced Association with Supervoxels in Multiple Hypothesis Tracking. In IEEE Access, 2018.
JCSTD 34.	37.8	47.4 ±8.3	41.1	14.4%	36.4%	8,076	86,638	1,266 (24.1)	2,697 (51.4)	8.8	Public
	W. Tian, M. Lauer, L. Chen. Online Multi-Object Tracking Using Joint Domain Information in Traffic Scenarios. In IEEE Transactions on Intelligent Transportation Systems, 2019.
ASTT 35.	29.2	47.2 ±9.6	44.3	16.3%	41.6%	4,680	90,877	633 (12.6)	814 (16.2)	0.5	Public
	Yi Tao el al., “Adaptive Spatio-temporal Model Based Multiple Object Tracking Considering a Moving Camera[C]”, International Conference on Universal Village (UV), 2018.
eHAF16 36.	28.7	47.2 ±16.8	52.4	18.6%	42.8%	12,586	83,107	542 (10.0)	787 (14.5)	0.5	Public
	H. Sheng, Y. Zhang, J. Chen, Z. Xiong, J. Zhang. Heterogeneous Association Graph Fusion for Target Association in Multiple Object Tracking. In IEEE Transactions on Circuits and Systems for Video Technology, 2018.
AMIR 37.	32.3	47.2 ±7.7	46.3	14.0%	41.6%	2,681	92,856	774 (15.8)	1,675 (34.1)	1.0	Public
	A. Sadeghian, A. Alahi, S. Savarese. Tracking The Untrackable: Learning To Track Multiple Cues with Long-Term Dependencies. In ICCV, 2017.
MCjoint 38.	28.1	47.1 ±10.8	52.3	20.4%	46.9%	6,703	89,368	370 (7.3)	598 (11.7)	0.6	Public
	}@article{DBLP:journals/corr/KeuperTYABS16, author = {Margret Keuper and Siyu Tang and Zhongjie Yu and Bjoern Andres and Thomas Brox and Bernt Schiele}, title = {A Multi-cut Formulation for Joint Segmentation and Tracking of Multiple Objects}, journal = {CoRR}, volume = {abs/1607.06317}, year = {2016}, url = {http://arxiv.org/abs/1607.06317}, timestamp = {Wed, 07 Jun 2017 14:41:31 +0200}, biburl = {http://dblp.uni-trier.de/rec/bib/journals/corr/KeuperTYABS16}, bibsource = {dblp computer science bibliography, http://dblp.org} }
NOMT 39.	26.5	46.4 ±9.9	53.3	18.3%	41.4%	9,753	87,565	359 (6.9)	504 (9.7)	2.6	Public
	W. Choi. Near-Online Multi-target Tracking with Aggregated Local Flow Descriptor. In ICCV, 2015.
JMC 40.	33.3	46.3 ±9.0	46.3	15.5%	39.7%	6,373	90,914	657 (13.1)	1,114 (22.2)	0.8	Public
	S. Tang, B. Andres, M. Andriluka, B. Schiele. Multi-Person Tracking by Multicuts and Deep Matching. In BMTT, 2016.
Tracker	Avg Rank	MOTA	IDF1	MT	ML	FP	FN	ID Sw.	Frag	Hz	Detector
DMMOT 41.	32.3	46.1 ±11.1	54.8	17.4%	42.7%	7,909	89,874	532 (10.5)	1,616 (31.9)	0.3	Public
	J. Zhu, H. Yang, N. Liu, M. Kim, W. Zhang, M. Yang. Online Multi-Object Tracking with Dual Matching Attention Networks. In ECCV, 2018.
STAM16 42.	40.4	46.0 ±9.1	50.0	14.6%	43.6%	6,895	91,117	473 (9.5)	1,422 (28.4)	0.2	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.
deepS2 43.	31.5	46.0 ±8.2	46.5	15.5%	42.6%	5,124	92,697	693 (14.1)	759 (15.4)	0.7	Public
	ID 32
RAR16pub 44.	41.3	45.9 ±9.7	48.8	13.2%	41.9%	6,871	91,173	648 (13.0)	1,992 (39.8)	0.9	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.
MHT_DAM 45.	34.3	45.8 ±8.9	46.1	16.2%	43.2%	6,412	91,758	590 (11.9)	781 (15.7)	0.8	Public
	C. Kim, F. Li, A. Ciptadi, J. Rehg. Multiple Hypothesis Tracking Revisited. In ICCV, 2015.
MTDF 46.	45.3	45.7 ±11.2	40.1	14.1%	36.4%	12,018	84,970	1,987 (37.2)	3,377 (63.2)	1.5	Public
	Z. Fu, F. Angelini, J. Chambers, S. Naqvi. Multi-Level Cooperative Fusion of GM-PHD Filters for Online Multiple Human Tracking. In IEEE Transactions on Multimedia, 2019.
TBNMF16 47.	36.4	45.6 ±8.9	46.0	13.4%	43.5%	4,230	94,435	584 (12.1)	1,229 (25.5)	7.9	Public
	Anonymous submission
INTERA_MOT 48.	30.4	45.4 ±8.6	47.7	18.1%	38.7%	13,407	85,547	600 (11.3)	930 (17.5)	4.3	Public
	L. Lan, X. Wang, S. Zhang, D. Tao, W. Gao, T. Huang. Interacting Tracklets for Multi-object Tracking. In IEEE Transactions on Image Processing, 2018.
EDMT 49.	32.6	45.3 ±9.1	47.9	17.0%	39.9%	11,122	87,890	639 (12.3)	946 (18.3)	1.8	Public
	J. Chen, H. Sheng, Y. Zhang, Z. Xiong. Enhancing Detection Model for Multiple Hypothesis Tracking. In BMTT-PETS CVPRw, 2017.
DCCRF16 50.	41.7	44.8 ±9.8	39.7	14.1%	42.3%	5,613	94,133	968 (20.0)	1,378 (28.5)	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.
Tracker	Avg Rank	MOTA	IDF1	MT	ML	FP	FN	ID Sw.	Frag	Hz	Detector
TBSS 51.	42.9	44.6 ±9.3	42.6	12.3%	43.9%	4,136	96,128	790 (16.7)	1,419 (30.0)	3.0	Public
	X. Zhou, P. Jiang, Z. Wei, H. Dong, F. Wang. Online Multi-Object Tracking with Structural Invariance Constraint. In BMVC, 2018.
QuadMOT16 52.	42.5	44.1 ±9.4	38.3	14.6%	44.9%	6,388	94,775	745 (15.5)	1,096 (22.8)	1.8	Public
	J. Son, M. Baek, M. Cho, B. Han. Multi-Object Tracking with Quadruplet Convolutional Neural Networks. In CVPR, 2017.
CDA_DDALv2 53.	41.3	43.9 ±7.8	45.1	10.7%	44.4%	6,450	95,175	676 (14.1)	1,795 (37.6)	0.5	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.
CSAHD 54.	40.9	43.7 ±11.6	45.7	10.5%	46.1%	8,318	93,273	984 (20.1)	2,164 (44.3)	23.2	Public
	Anonymous submission
LFNF16 55.	44.1	43.6 ±11.0	41.6	13.3%	45.7%	6,616	95,363	836 (17.5)	938 (19.7)	0.6	Public
	Sheng H, Hao L, Chen J, et al. Robust Local Effective Matching Model for Multi-Target Tracking. In PCM, 2017
oICF 56.	42.5	43.2 ±10.2	49.3	11.3%	48.5%	6,651	96,515	381 (8.1)	1,404 (29.8)	0.4	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.
MHT_bLSTM6 57.	42.9	42.1 ±9.7	47.8	14.9%	44.4%	11,637	93,172	753 (15.4)	1,156 (23.6)	1.8	Public
	C. Kim, F. Li, J. Rehg. Multi-object Tracking with Neural Gating Using Bilinear LSTM. In ECCV, 2018.
OST16 58.	46.8	41.5 ±9.2	39.1	10.7%	45.6%	5,919	99,709	1,056 (23.3)	1,487 (32.8)	4.7	Public
	Anonymous submission
LINF1 59.	40.8	41.0 ±9.5	45.7	11.6%	51.3%	7,896	99,224	430 (9.4)	963 (21.1)	4.2	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.
PHD_GSDL16 60.	47.3	41.0 ±8.9	43.1	11.3%	41.5%	6,498	99,257	1,810 (39.7)	3,650 (80.1)	8.3	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.
Tracker	Avg Rank	MOTA	IDF1	MT	ML	FP	FN	ID Sw.	Frag	Hz	Detector
PMPTracker 61.	49.7	40.3 ±11.7	38.2	10.4%	42.0%	10,071	97,524	1,343 (28.9)	2,764 (59.4)	148.0	Public
	Light version of PTZ camera Mutiple People Tracker
AM_ADM 62.	44.2	40.1 ±10.1	43.8	7.1%	46.2%	8,503	99,891	789 (17.5)	1,736 (38.4)	5.8	Public
	S. Lee, M. Kim, S. Bae, Learning Discriminative Appearance Models for Online Multi-Object Tracking with Appearance Discriminability Measures, In IEEE Access, 2018.
D_cost16 63.	38.4	39.9 ±9.1	35.3	8.7%	50.2%	1,133	107,586	790 (19.3)	824 (20.1)	8.5	Public
	Anonymous submission
TST_PLS 64.	52.5	39.7 ±11.1	43.3	6.7%	47.4%	8,447	100,728	783 (17.5)	1,730 (38.7)	4.0	Public
	Anonymous submission
EAMTT_pub 65.	45.6	38.8 ±8.5	42.4	7.9%	49.1%	8,114	102,452	965 (22.0)	1,657 (37.8)	11.8	Public
	R. Sanchez-Matilla, F. Poiesi, A. Cavallaro "Multi-target tracking with strong and weak detections" in BMTT ECCVw 2016
OVBT 66.	59.3	38.4 ±8.8	37.8	7.5%	47.3%	11,517	99,463	1,321 (29.1)	2,140 (47.1)	0.3	Public
	Y. Ban, S. Ba, X. Alameda-Pineda, R. Horaud. Tracking Multiple Persons Based on a Variational Bayesian Model. In BMTT 2016, .
HAM_ACT16 67.	39.8	38.1 ±8.2	43.3	7.8%	54.4%	6,976	105,434	418 (9.9)	707 (16.8)	8.0	Public
GMMCP 68.	51.4	38.1 ±7.8	35.5	8.6%	50.9%	6,607	105,315	937 (22.2)	1,669 (39.5)	0.5	Public
	A. Dehghan, S. Assari, M. Shah.. GMMCP-Tracker:Globally Optimal Generalized Maximum Multi Clique Problem for Multiple Object Tracking. In CVPR, 2015.
YT16 69.	52.3	37.8 ±8.8	31.1	8.8%	46.1%	4,384	106,365	2,655 (63.7)	2,750 (66.0)	12.1	Public
	Anonymous submission
LTTSC-CRF 70.	49.8	37.6 ±9.9	42.1	9.6%	55.2%	11,969	101,343	481 (10.8)	1,012 (22.8)	0.6	Public
	N. Le, A. Heili, M. Odobez. Long-Term Time-Sensitive Costs for CRF-Based Tracking by Detection. In ECCVw, 2016.
Tracker	Avg Rank	MOTA	IDF1	MT	ML	FP	FN	ID Sw.	Frag	Hz	Detector
HISP_T2 71.	50.8	37.2 ±8.6	29.7	7.6%	50.7%	3,323	108,859	2,370 (58.8)	2,234 (55.4)	4.8	Public
	Anonymous submission
JCmin_MOT 72.	42.3	36.7 ±9.1	36.2	7.5%	54.4%	2,936	111,890	667 (17.3)	831 (21.5)	14.8	Public
	M. Abhijeet Boragule. Joint Cost Minimization for Multi-Object Tracking. In 2017 IEEE International Conference on Advanced Vide and Signale Based Surveillance, 2017.
HISP_T 73.	53.3	35.9 ±8.5	28.9	7.8%	50.1%	6,412	107,918	2,594 (63.6)	2,298 (56.3)	4.8	Public
	N. Baisa. Online Multi-target Visual Tracking using a HISP Filter. In Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5: VISAPP,, 2018.
LP2D 74.	45.5	35.7 ±10.1	34.2	8.7%	50.7%	5,084	111,163	915 (23.4)	1,264 (32.4)	49.3	Public
	MOT baseline: Linear programming on 2D image coordinates.
cm_test 75.	44.0	35.4 ±20.2	40.3	6.5%	71.4%	4,427	112,889	402 (10.6)	1,176 (30.9)	1.6	Public
	Anonymous submission
GM_PHD_DAL 76.	54.3	35.1 ±9.1	26.6	7.0%	51.4%	2,350	111,886	4,047 (104.8)	5,338 (138.2)	3.5	Public
	Anonymous submission
DRT 77.	44.9	34.7 ±11.4	41.1	6.3%	61.8%	6,992	111,617	460 (11.9)	1,127 (29.1)	6.2	Public
	Anonymous submission
GM_PHD_Dl 78.	55.3	34.3 ±9.1	20.5	7.1%	51.5%	2,350	111,886	5,605 (145.1)	5,357 (138.7)	3.5	Public
	Anonymous submission
RNN_A_P 79.	58.0	34.0 ±8.6	33.7	7.9%	51.0%	8,562	109,269	2,479 (61.9)	3,393 (84.7)	19.7	Public
	Anonymous submission
TDP 80.	49.8	33.9 ±10.2	40.4	6.2%	62.2%	6,709	113,249	480 (12.7)	1,105 (29.2)	9.7	Public
	Anonymous submission
Tracker	Avg Rank	MOTA	IDF1	MT	ML	FP	FN	ID Sw.	Frag	Hz	Detector
GM_PHD_e17 81.	56.3	33.8 ±8.9	25.3	6.3%	54.9%	1,766	115,130	3,778 (102.5)	3,874 (105.1)	3.3	Public
	Anonymous submission
TBD 82.	61.2	33.7 ±9.2	0.0	7.2%	54.2%	5,804	112,587	2,418 (63.2)	2,252 (58.9)	1.3	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.
GM_PHD_N1T 83.	54.2	33.3 ±8.9	25.5	5.5%	56.0%	1,750	116,452	3,499 (96.8)	3,594 (99.5)	9.9	Public
	N. Baisa, A. Wallace. Development of a N-type GM-PHD filter for multiple target, multiple type visual tracking. In Journal of Visual Communication and Image Representation, 2019.
CEM 84.	49.7	33.2 ±7.9	0.0	7.8%	54.4%	6,837	114,322	642 (17.2)	731 (19.6)	0.3	Public
	A. Milan, S. Roth, K. Schindler. Continuous Energy Minimization for Multitarget Tracking. In IEEE TPAMI, 2014.
CppSORT 85.	49.9	31.5 ±9.0	27.7	4.3%	59.9%	3,048	120,278	1,587 (46.6)	2,239 (65.8)	687.1	Public
	S. Murray. Real-Time Multiple Object Tracking - A Study on the Importance of Speed. In arXiv preprint arXiv:1709.03572, 2017.
GMPHD_HDA 86.	44.0	30.5 ±6.9	33.4	4.6%	59.7%	5,169	120,970	539 (16.0)	731 (21.7)	13.6	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.
SMOT 87.	68.8	29.7 ±7.3	0.0	5.3%	47.7%	17,426	107,552	3,108 (75.8)	4,483 (109.3)	0.2	Public
	C. Dicle, O. Camps, M. Sznaier. The Way They Move: Tracking Targets with Similar Appearance. In ICCV, 2013.
GCK 88.	57.3	28.7 ±8.5	30.6	3.4%	51.0%	21,436	106,424	2,217 (53.3)	3,277 (78.7)	25.1	Public
	Anonymous submission
DCOR 89.	50.0	28.3 ±9.0	21.7	3.4%	63.9%	1,618	128,345	849 (28.7)	2,592 (87.5)	32.9	Public
	Anonymous submission
JPDA_m 90.	44.2	26.2 ±6.1	0.0	4.1%	67.5%	3,689	130,549	365 (12.9)	638 (22.5)	22.2	Public
	H. Rezatofighi, A. Milan, Z. Zhang, Q. Shi, A. Dick, I. Reid. Joint Probabilistic Data Association Revisited. In ICCV, 2015.
Tracker	Avg Rank	MOTA	IDF1	MT	ML	FP	FN	ID Sw.	Frag	Hz	Detector
DP_NMS 91.	42.6	26.2 ±9.3	31.2	4.1%	67.5%	3,689	130,557	365 (12.9)	638 (22.5)	5.9	Public
	H. Pirsiavash, D. Ramanan, C. Fowlkes. Globally-Optimal Greedy Algorithms for Tracking a Variable Number of Objects. In CVPR, 2011.

Difficulty Analysis

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

MOT16-03

(53.0% MOTA)

MOT16-06

(44.3% MOTA)

MOT16-07

(38.0% MOTA)

...

...

MOT16-08

(29.8% MOTA)

MOT16-14

(24.1% MOTA)