MOT16 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
RC_cluster 1.	11.2	53.8 ±8.7	46.9	21.6%	36.9%	4,764	78,948	518 (9.1)	795 (14.0)	8.5	Public
	Anonymous submission
PDetTracId 2.	21.7	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
eTC 3.	15.7	49.2 ±9.1	56.1	17.3%	40.3%	8,400	83,702	606 (11.2)	882 (16.3)	0.7	Public
	Anonymous submission
TPM 4.	20.3	49.1 ±9.1	46.9	20.0%	38.9%	9,038	83,031	679 (12.5)	850 (15.6)	0.8	Public
	Anonymous submission
AFN 5.	18.0	49.0 ±10.2	48.2	19.1%	35.7%	9,508	82,506	899 (16.4)	1,383 (25.3)	0.6	Public
	Paper ID 4411
LMP 6.	15.5	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.
TLMHT 7.	16.3	48.7 ±8.6	55.3	15.7%	44.5%	6,632	86,504	413 (7.9)	642 (12.2)	4.8	Public
	TCSVT-02160-2018
TripBFT 8.	19.3	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
TSN 9.	21.2	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
GCRA 10.	19.2	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.
Tracker	Avg Rank	MOTA	IDF1	MT	ML	FP	FN	ID Sw.	Frag	Hz	Detector
TripT 11.	19.8	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
FWT 12.	22.0	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.
MOTDT 13.	20.5	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 14.	16.3	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.
JCSTD 15.	24.5	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
	Anonymous submission
eHAF16 16.	18.4	47.2 ±16.8	52.4	18.6%	42.8%	12,586	83,107	542 (10.0)	787 (14.5)	0.5	Public
	TCSVT-02141-2018
AMIR 17.	20.3	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 18.	17.4	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 19.	16.4	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 20.	20.4	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 21.	20.8	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.
STAM16 22.	26.0	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.
RAR16pub 23.	26.4	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.
MHT_DAM 24.	21.3	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 25.	30.2	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
	Anonymous submission
INTERA_MOT 26.	19.2	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.
EDMT 27.	19.9	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 28.	27.7	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.
TBSS 29.	28.0	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 30.	27.6	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.
Tracker	Avg Rank	MOTA	IDF1	MT	ML	FP	FN	ID Sw.	Frag	Hz	Detector
CDA_DDALv2 31.	27.3	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 32.	28.0	43.7 ±11.6	45.7	10.5%	46.1%	8,318	93,273	984 (20.1)	2,164 (44.3)	7.0	Public
	Anonymous submission
deepS2 33.	26.1	43.6 ±8.1	40.4	15.4%	41.9%	8,819	93,095	871 (17.8)	851 (17.4)	0.7	Public
	ID 32
LFNF16 34.	28.9	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 35.	28.3	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 36.	27.8	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.
TBNMF16 37.	32.0	42.0 ±9.2	37.5	10.4%	44.9%	4,966	99,778	1,085 (24.0)	1,400 (30.9)	4.5	Public
	Anonymous submission
LINF1 38.	26.8	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.
PRT 39.	30.7	40.8 ±13.0	44.2	13.7%	38.3%	15,143	91,792	1,051 (21.2)	2,210 (44.5)	6.2	Public
	Anonymous submission
AM_ADM 40.	30.0	40.1 ±10.1	43.8	7.1%	46.2%	8,503	99,891	789 (17.5)	1,736 (38.4)	5.8	Public
	Anonymous submission
Tracker	Avg Rank	MOTA	IDF1	MT	ML	FP	FN	ID Sw.	Frag	Hz	Detector
TST_PLS 41.	35.8	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 42.	30.7	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
OVBT 43.	40.7	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 44.	26.5	38.1 ±8.2	43.3	7.8%	54.4%	6,976	105,434	418 (9.9)	707 (16.8)	8.0	Public
GMMCP 45.	35.4	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 46.	35.8	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 47.	33.8	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.
HISP_T2 48.	35.1	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 49.	28.5	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_T 50.	36.7	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.
Tracker	Avg Rank	MOTA	IDF1	MT	ML	FP	FN	ID Sw.	Frag	Hz	Detector
LP2D 51.	31.4	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 52.	30.2	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
DRT 53.	30.7	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
TDP 54.	34.1	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
TBD 55.	42.0	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 56.	37.7	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 CoRR, 2017.
CEM 57.	34.0	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.
DWET 58.	33.3	32.2 ±10.4	38.3	6.2%	63.0%	7,297	115,780	603 (16.5)	1,184 (32.4)	11.3	Public
	Anonymous submission
CppSORT 59.	35.3	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.
GMPHD_HDA 60.	30.5	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.
Tracker	Avg Rank	MOTA	IDF1	MT	ML	FP	FN	ID Sw.	Frag	Hz	Detector
SMOT 61.	48.0	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 62.	40.3	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
JPDA_m 63.	30.6	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.
DP_NMS 64.	29.3	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

(52.1% MOTA)

MOT16-06

(43.1% MOTA)

MOT16-07

(37.7% MOTA)

...

...

MOT16-08

(29.4% MOTA)

MOT16-14

(23.5% MOTA)