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
test_trker 1.	51.7	0.0 ±0.0	0.0	0.0%	100.0%	7	182,326	0 (nan)	0 (nan)	22.3	Public
	Anonymous submission
DP_NMS 2.	47.9	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.
JPDA_m 3.	49.0	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.
retrack 4.	52.1	26.2 ±13.2	31.0	17.4%	26.6%	52,610	78,845	3,042 (53.6)	4,835 (85.2)	22.3	Public
	Anonymous submission
MHT_ReID 5.	54.8	27.1 ±47.2	36.4	30.6%	31.4%	13,068	118,829	1,071 (30.8)	1,141 (32.8)	0.5	Public
	Anonymous submission
DCOR 6.	56.0	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
SMOT 7.	78.1	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.
GMPHD_HDA 8.	48.9	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.
CppSORT 9.	55.9	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.
CEM 10.	55.7	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.
Tracker	Avg Rank	MOTA	IDF1	MT	ML	FP	FN	ID Sw.	Frag	Hz	Detector
GM_PHD_N1T 11.	61.2	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.
TBD 12.	69.8	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_e17 13.	63.3	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
RNN_A_P 14.	66.2	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
GM_PHD_Dl 15.	62.8	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
GM_PHD_DAL 16.	61.8	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
	N. Baisa. Online Multi-object Visual Tracking using a GM-PHD Filter with Deep Appearance Learning. In 22nd International Conference on Information Fusion, 2019.
LP2D 17.	52.3	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.
HISP_T 18.	61.3	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.
JCmin_MOT 19.	48.7	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.
PHD_T 20.	61.8	37.1 ±8.7	39.4	10.1%	45.7%	6,679	104,894	3,095 (72.9)	3,671 (86.4)	9.9	Public
	Anonymous submission
Tracker	Avg Rank	MOTA	IDF1	MT	ML	FP	FN	ID Sw.	Frag	Hz	Detector
HISP_DAL 21.	59.2	37.4 ±8.8	30.5	7.6%	50.9%	3,222	108,865	2,101 (52.1)	2,151 (53.4)	3.3	Public
	N. Baisa. Robust Online Multi-target Visual Tracking using a HISP Filter with Discriminative Deep Appearance Learning. In CoRR, 2019.
LTTSC-CRF 22.	57.7	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.
GMMCP 23.	59.8	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.
HAM_ACT16 24.	46.5	38.1 ±8.2	43.3	7.8%	54.4%	6,976	105,434	418 (9.9)	707 (16.8)	8.0	Public
OVBT 25.	68.8	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, .
EAMTT_pub 26.	54.3	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
D_cost16 27.	46.0	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
AM_ADM 28.	52.8	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.
PMPTracker 29.	58.4	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
PHD_GSDL16 30.	56.4	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
LINF1 31.	48.1	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.
OST16 32.	55.6	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
TestUnsup 33.	49.5	41.5 ±9.0	44.9	13.7%	43.5%	12,596	93,404	643 (13.2)	796 (16.3)	19.7	Public
	Multi Object Tracking using Deep Structural Cost Minimization in Data Association
MHT_bLSTM6 34.	52.0	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.
oICF 35.	50.8	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.
LFNF16 36.	52.5	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
CDA_DDALv2 37.	49.8	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.
QuadMOT16 38.	51.7	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.
TBSS 39.	52.2	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.
DCCRF16 40.	49.9	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
EDMT 41.	40.8	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.
INTERA_MOT 42.	37.8	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.
MTDF 43.	54.9	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.
MHT_DAM 44.	41.8	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.
RAR16pub 45.	50.4	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.
deepS2 46.	39.3	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
STAM16 47.	49.1	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.
DMAN 48.	39.8	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.
DD_TAMA16 49.	36.3	46.2 ±8.4	49.4	14.1%	44.0%	5,126	92,367	598 (12.1)	1,127 (22.8)	6.5	Public
	Y. Yoon, D. Kim, K. Yoon, Y. Song, M. Jeon. Online Multiple Pedestrian Tracking using Deep Temporal Appearance Matching Association. In arXiv:1907.00831, 2019.
JMC 50.	41.3	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
NOMT 51.	33.5	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.
YOONKJ16 52.	43.3	47.0 ±8.4	50.1	16.5%	41.8%	7,901	88,179	627 (12.1)	945 (18.3)	3.5	Public
	Anonymous submission
MCjoint 53.	34.1	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} }
AMIR 54.	41.3	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.
eHAF16 55.	35.5	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.
ASTT 56.	36.3	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.
JCSTD 57.	46.1	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.
EAGS16 58.	31.2	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.
NLLMPa 59.	34.8	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.
MOTDT 60.	39.8	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.
Tracker	Avg Rank	MOTA	IDF1	MT	ML	FP	FN	ID Sw.	Frag	Hz	Detector
FWT 61.	41.8	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.
SRPN16 62.	45.6	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
GCRA 63.	39.2	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.
AOReid 64.	34.8	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
MOTPP 65.	35.0	48.3 ±8.7	45.4	18.6%	40.1%	7,378	86,181	661 (12.5)	834 (15.8)	11.8	Public
	Anonymous submission
MOTPPF 66.	31.3	48.4 ±8.8	48.5	19.1%	39.8%	9,152	84,266	595 (11.1)	802 (14.9)	11.8	Public
	Anonymous submission
TLMHT 67.	33.9	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.
DeepMP16 68.	29.3	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
LMP 69.	31.6	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.
KCF16 70.	41.6	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.
Tracker	Avg Rank	MOTA	IDF1	MT	ML	FP	FN	ID Sw.	Frag	Hz	Detector
SCNet 71.	45.9	48.8 ±8.8	51.5	15.4%	33.5%	11,666	80,725	891 (16.0)	2,100 (37.7)	0.3	Public
	Anonymous submission
DAST 72.	33.4	48.9 ±8.4	53.2	15.2%	36.2%	9,987	82,427	838 (15.3)	1,936 (35.3)	8.7	Public
	Anonymous submission
CRF_RNN16 73.	28.8	49.0 ±7.2	53.9	18.1%	35.8%	8,495	83,838	621 (11.5)	1,252 (23.2)	1.5	Public
	Anonymous submission
AFN 74.	34.1	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.
MOTHP 75.	31.9	49.1 ±9.1	46.9	20.0%	38.9%	9,038	83,031	679 (12.5)	850 (15.6)	11.8	Public
	Anonymous submission
eTC 76.	31.5	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.
LSST16O 77.	38.4	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
HCC 78.	27.6	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.
STCG 79.	30.3	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
siameseCos 80.	34.3	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
Tracker	Avg Rank	MOTA	IDF1	MT	ML	FP	FN	ID Sw.	Frag	Hz	Detector
CMT16 81.	23.3	49.8 ±9.0	59.2	16.6%	43.6%	9,229	81,882	365 (6.6)	617 (11.2)	6.3	Public
	#Submission: TIP-21190-2019
NOTA 82.	27.8	49.8 ±8.3	55.3	17.9%	37.7%	7,248	83,614	614 (11.3)	1,372 (25.3)	19.2	Public
	L. Chen, H. Ai, R. Chen, Z. Zhuang. Aggregate Tracklet Appearance Features for Multi-Object Tracking. In IEEE Signal Processing Letters, 2019.
RTT 83.	40.6	49.9 ±8.0	49.3	19.0%	32.8%	9,927	80,406	955 (17.1)	2,247 (40.2)	1.8	Public
	Anonymous submission
ENFT 84.	21.3	50.0 ±8.2	54.6	17.8%	41.1%	8,214	82,541	479 (8.8)	724 (13.2)	22.3	Public
	Anonymous submission
pairwise16 85.	27.6	50.0 ±65.9	52.4	19.4%	38.7%	10,995	79,568	628 (11.1)	939 (16.7)	22.3	Public
	Anonymous submission
MEN 86.	34.9	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
MOT_FILTER 87.	30.7	50.2 ±12.9	46.8	17.9%	39.7%	5,267	84,812	664 (12.4)	978 (18.3)	11.8	Public
	Anonymous submission
ENFT16 88.	25.5	50.3 ±8.3	55.0	19.2%	39.8%	8,341	81,843	490 (8.9)	754 (13.7)	0.4	Public
	BUAA
CRF_TRACK 89.	26.7	50.3 ±7.9	54.4	18.3%	35.7%	7,148	82,746	702 (12.9)	1,387 (25.4)	1.5	Public
	Anonymous submission
CRFTrack16 90.	27.3	50.3 ±7.9	54.4	18.3%	35.7%	7,148	82,746	702 (12.9)	1,387 (25.4)	1.5	Public
	Anonymous submission
Tracker	Avg Rank	MOTA	IDF1	MT	ML	FP	FN	ID Sw.	Frag	Hz	Detector
MOTHPCLEAN 91.	27.4	50.4 ±9.4	47.0	19.1%	39.5%	5,332	84,505	657 (12.2)	862 (16.1)	11.8	Public
	Anonymous submission
PV 92.	39.0	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
MOTPP16 93.	28.8	50.5 ±9.7	47.2	19.6%	39.4%	5,939	83,694	638 (11.8)	823 (15.2)	3.0	Public
	Anonymous submission
UTA 94.	35.7	50.6 ±7.9	50.4	18.3%	33.5%	7,752	81,584	722 (13.1)	2,196 (39.7)	5.0	Public
	Anonymous submission
TPM 95.	30.9	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
HDTR 96.	22.3	53.6 ±8.7	46.6	21.2%	37.0%	4,714	79,353	618 (10.9)	833 (14.7)	3.6	Public
Tracktor16 97.	29.9	54.4 ±12.0	52.5	19.0%	36.9%	3,280	79,149	682 (12.1)	1,480 (26.2)	1.5	Public
	P. Bergmann, T. Meinhardt, L. Leal-Taixé. Tracking without bells and whistles. In ICCV, 2019.
MTT_TPR 98.	30.4	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
MHT___ReID 99.	35.0	56.4 ±11.6	54.2	39.7%	17.4%	23,791	54,169	1,478 (21.0)	1,547 (22.0)	0.5	Public
	Anonymous submission
ReTrack16 100.	27.2	57.0 ±12.3	54.2	21.9%	34.3%	4,446	73,258	688 (11.5)	1,543 (25.8)	0.8	Public
	Anonymous submission
Tracker	Avg Rank	MOTA	IDF1	MT	ML	FP	FN	ID Sw.	Frag	Hz	Detector
DpTrack 101.	25.5	59.3 ±18.7	52.8	27.4%	24.6%	8,566	63,603	2,045 (31.4)	1,555 (23.9)	10.4	Public
	Anonymous submission
DS_v2 102.	21.9	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

Difficulty Analysis

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

MOT16-03

(51.9% MOTA)

MOT16-06

(45.0% MOTA)

MOT16-07

(38.9% MOTA)

...

...

MOT16-08

(30.0% MOTA)

MOT16-14

(24.8% MOTA)