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.	59.7	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.	55.8	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.	56.4	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.
MHT_ReID 4.	63.6	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 5.	65.4	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 6.	90.6	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 7.	56.7	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 8.	65.3	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 9.	64.3	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.
GM_PHD_N1T 10.	71.7	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.
Tracker	Avg Rank	MOTA	IDF1	MT	ML	FP	FN	ID Sw.	Frag	Hz	Detector
KVIOU16 11.	62.8	33.4 ±9.7	32.6	5.9%	59.6%	2,764	117,971	760 (21.5)	1,473 (41.7)	29.6	Public
	Anonymous submission
TBD 12.	81.3	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.	73.8	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.	77.7	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.	73.3	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
GM_PHD_DAL 16.	72.3	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
	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.	61.0	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.	72.1	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.	56.5	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_DAL 20.	69.8	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.
Tracker	Avg Rank	MOTA	IDF1	MT	ML	FP	FN	ID Sw.	Frag	Hz	Detector
GoturnM16 21.	75.7	37.5 ±7.5	25.1	8.4%	46.5%	17,746	92,867	3,277 (66.8)	2,994 (61.0)	3.9	Public
	Anonymous submission
LTTSC-CRF 22.	67.3	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.	70.3	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.	54.6	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.	80.5	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.	64.7	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
SDMT 27.	59.3	39.6 ±8.3	42.3	11.7%	49.1%	11,130	98,343	602 (13.1)	772 (16.8)	19.8	Public
	M. Thoreau, N. Kottege. Deep Similarity Metric Learning for Real-Time Pedestrian Tracking. In arXiv, 2018.
D_cost16 28.	54.3	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 29.	62.3	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 30.	68.5	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
Tracker	Avg Rank	MOTA	IDF1	MT	ML	FP	FN	ID Sw.	Frag	Hz	Detector
PHD_T 31.	62.8	40.3 ±9.0	48.3	11.6%	43.1%	7,147	100,895	815 (18.2)	2,446 (54.8)	9.9	Public
	Anonymous submission
GMPHD_ReId 32.	57.2	40.3 ±9.3	48.3	11.6%	43.1%	7,147	100,895	815 (18.2)	2,446 (54.8)	20.4	Public
	Anonymous submission
PHD_GSDL16 33.	67.0	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.
LINF1 34.	56.3	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 35.	65.7	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 36.	57.8	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 37.	60.5	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.
AEb 38.	39.3	42.9 ±11.0	48.7	15.3%	49.0%	4,487	99,310	375 (8.2)	1,334 (29.3)	22.3	Public
	Anonymous submission
oICF 39.	59.0	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 40.	61.4	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
Tracker	Avg Rank	MOTA	IDF1	MT	ML	FP	FN	ID Sw.	Frag	Hz	Detector
CDA_DDALv2 41.	58.3	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.
SRPN 42.	63.5	44.0 ±10.7	36.6	15.5%	45.7%	18,784	82,318	1,047 (19.1)	1,118 (20.4)	3.9	Public
	Anonymous submission
QuadMOT16 43.	59.8	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.
OTCD_1 44.	57.8	44.4 ±10.8	45.6	11.6%	47.6%	5,759	94,927	759 (15.8)	1,787 (37.3)	17.6	Public
	Q. Liu, B. Liu, Y. Wu, W. Li, N. Yu. Real-Time Online Multi-Object Tracking in Compressed Domain. In IEEE Access, 2019.
TBSS 45.	61.1	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 46.	58.4	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.
EDMT 47.	47.1	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 48.	44.2	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 49.	65.0	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 50.	48.8	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.
Tracker	Avg Rank	MOTA	IDF1	MT	ML	FP	FN	ID Sw.	Frag	Hz	Detector
RAR16pub 51.	58.3	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.
deepS2 52.	45.4	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 53.	56.9	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 54.	46.2	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 55.	42.2	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 56.	47.8	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.
NOMT 57.	39.6	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 58.	50.8	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 59.	39.6	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 60.	48.6	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.
Tracker	Avg Rank	MOTA	IDF1	MT	ML	FP	FN	ID Sw.	Frag	Hz	Detector
eHAF16 61.	41.3	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 62.	42.4	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 63.	55.1	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 64.	35.8	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 65.	40.4	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 66.	46.8	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.
FWT 67.	49.8	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 68.	53.7	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 69.	46.1	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.
AOReid 70.	41.9	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
Tracker	Avg Rank	MOTA	IDF1	MT	ML	FP	FN	ID Sw.	Frag	Hz	Detector
MOTPP 71.	41.4	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 72.	36.8	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
STRN_MOT16 73.	45.4	48.5 ±8.5	53.9	17.0%	34.9%	9,038	84,178	747 (13.9)	2,919 (54.2)	13.5	Public
	J. Xu, Y. Cao, Z. Zhang, H. Hu. Spatial-Temporal Relation Networks for Multi-Object Tracking. In ICCV, 2019.
TLMHT 74.	40.2	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 75.	34.7	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 76.	36.8	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.
KCF16 77.	48.8	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.
DAST 78.	40.5	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 79.	33.9	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 80.	41.3	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.
Tracker	Avg Rank	MOTA	IDF1	MT	ML	FP	FN	ID Sw.	Frag	Hz	Detector
MOTHP 81.	37.7	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 82.	36.5	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 Proceedings of the 27th ACM International Conference on Multimedia, 2019.
LSST16O 83.	46.1	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 84.	32.2	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.
STCG 85.	35.9	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 86.	40.3	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
TLO16 87.	44.2	49.8 ±10.0	47.8	16.6%	40.6%	6,085	84,623	782 (14.6)	1,278 (23.8)	12.4	Public
	Anonymous submission
CMT16 88.	28.0	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 89.	32.8	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.
OMHT16 90.	44.6	49.8 ±9.9	46.7	16.1%	40.4%	6,244	84,342	888 (16.5)	1,332 (24.8)	12.4	Public
	Anonymous submission
Tracker	Avg Rank	MOTA	IDF1	MT	ML	FP	FN	ID Sw.	Frag	Hz	Detector
MMHT16 91.	41.9	49.9 ±9.8	47.3	16.2%	40.7%	6,110	84,455	823 (15.3)	1,289 (24.0)	12.4	Public
	Anonymous submission
RTT 92.	47.9	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 93.	25.1	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 94.	32.0	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
SCNet 95.	51.9	50.0 ±8.9	51.1	15.5%	34.1%	10,526	79,755	866 (15.4)	2,141 (38.1)	0.3	Public
	Anonymous submission
MEN 96.	41.0	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
TLO 97.	45.3	50.1 ±9.9	48.1	16.3%	40.7%	5,582	84,629	786 (14.7)	1,294 (24.1)	5.6	Public
	Anonymous submission
MOT_FILTER 98.	35.8	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 99.	29.2	50.3 ±8.3	55.0	19.2%	39.8%	8,341	81,843	490 (8.9)	754 (13.7)	0.4	Public
	BUAA
HTBT16 100.	31.9	50.3 ±8.2	55.0	19.2%	39.8%	8,341	81,843	490 (8.9)	754 (13.7)	0.2	Public
	Anonymous submission
Tracker	Avg Rank	MOTA	IDF1	MT	ML	FP	FN	ID Sw.	Frag	Hz	Detector
CRF_TRACK 101.	31.6	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 102.	32.3	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
MOTHPCLEAN 103.	32.2	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
TTL16 104.	44.3	50.4 ±10.3	50.1	17.4%	39.9%	8,491	81,156	807 (14.5)	1,251 (22.5)	6.7	Public
	Anonymous submission
PV 105.	46.3	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 106.	33.6	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 107.	41.7	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 108.	36.7	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 109.	26.4	53.6 ±8.7	46.6	21.2%	37.0%	4,714	79,353	618 (10.9)	833 (14.7)	3.6	Public
retrack 110.	34.0	53.9 ±13.0	52.7	20.3%	32.3%	6,999	76,251	818 (14.1)	2,613 (44.9)	22.3	Public
	Anonymous submission
Tracker	Avg Rank	MOTA	IDF1	MT	ML	FP	FN	ID Sw.	Frag	Hz	Detector
Tracktor16 111.	35.1	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 112.	36.1	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
MPNTrack16 113.	20.8	55.9 ±11.7	59.9	26.0%	35.6%	7,086	72,902	431 (7.2)	921 (15.3)	11.9	Public
	Anonymous submission
MHT___ReID 114.	40.8	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 115.	31.3	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
DpTrack 116.	30.5	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 117.	26.2	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
dpt_dpt 118.	24.3	61.3 ±10.7	60.4	32.1%	18.6%	12,411	57,481	739 (10.8)	1,960 (28.6)	148.0	Public
	Anonymous submission

Difficulty Analysis

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

MOT16-03

(53.0% MOTA)

MOT16-06

(45.7% MOTA)

MOT16-07

(40.0% MOTA)

...

...

MOT16-08

(30.6% MOTA)

MOT16-14

(25.6% MOTA)