MOT16 Results

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Tracker	Avg Rank	MOTA	IDF1	MT	ML	FP	FN	ID Sw.	Frag	Hz	Detector
dpt_dpt 1.	23.7	61.3 ±10.7	60.4	32.1%	18.6%	12,411	57,481	739 (10.8)	1,960 (28.6)	148.0	Public
dpt_dpt 1.	Anonymous submission
DS_v2 2.	25.1	59.3 ±12.9	57.5	24.2%	29.1%	7,465	65,810	887 (13.9)	2,738 (42.8)	39.4	Public
DS_v2 2.	Anonymous submission
DpTrack 3.	29.3	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
DpTrack 3.	Anonymous submission
ReTrack16 4.	30.1	57.0 ±12.3	54.2	21.9%	34.3%	4,446	73,258	688 (11.5)	1,543 (25.8)	0.8	Public
ReTrack16 4.	Anonymous submission
MHT___ReID 5.	39.4	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
MHT___ReID 5.	Anonymous submission
MTT_TPR 6.	34.3	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
MTT_TPR 6.	Anonymous submission
Tracktor16 7.	33.4	54.4 ±12.0	52.5	19.0%	36.9%	3,280	79,149	682 (12.1)	1,480 (26.2)	1.5	Public
Tracktor16 7.	P. Bergmann, T. Meinhardt, L. Leal-Taixé. Tracking without bells and whistles. In ICCV, 2019.
HDTR 8.	25.2	53.6 ±8.7	46.6	21.2%	37.0%	4,714	79,353	618 (10.9)	833 (14.7)	3.6	Public
HDTR 8.
TPM 9.	34.9	51.3 ±9.3	47.9	18.7%	40.8%	2,701	85,504	569 (10.7)	707 (13.3)	0.8	Public
TPM 9.	Anonymous submission
UTA 10.	39.8	50.6 ±7.9	50.4	18.3%	33.5%	7,752	81,584	722 (13.1)	2,196 (39.7)	5.0	Public
UTA 10.	Anonymous submission
Tracker	Avg Rank	MOTA	IDF1	MT	ML	FP	FN	ID Sw.	Frag	Hz	Detector
MOTPP16 11.	32.0	50.5 ±9.7	47.2	19.6%	39.4%	5,939	83,694	638 (11.8)	823 (15.2)	3.0	Public
MOTPP16 11.	Anonymous submission
PV 12.	44.3	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
PV 12.	Anonymous submission
TTL16 13.	42.2	50.4 ±10.3	50.1	17.4%	39.9%	8,491	81,156	807 (14.5)	1,251 (22.5)	6.7	Public
TTL16 13.	Anonymous submission
MOTHPCLEAN 14.	30.7	50.4 ±9.4	47.0	19.1%	39.5%	5,332	84,505	657 (12.2)	862 (16.1)	11.8	Public
MOTHPCLEAN 14.	Anonymous submission
CRF_TRACK 15.	30.0	50.3 ±7.9	54.4	18.3%	35.7%	7,148	82,746	702 (12.9)	1,387 (25.4)	1.5	Public
CRF_TRACK 15.	Anonymous submission
CRFTrack16 16.	30.7	50.3 ±7.9	54.4	18.3%	35.7%	7,148	82,746	702 (12.9)	1,387 (25.4)	1.5	Public
CRFTrack16 16.	Anonymous submission
ENFT16 17.	27.9	50.3 ±8.3	55.0	19.2%	39.8%	8,341	81,843	490 (8.9)	754 (13.7)	0.4	Public
ENFT16 17.	BUAA
MOT_FILTER 18.	34.2	50.2 ±12.9	46.8	17.9%	39.7%	5,267	84,812	664 (12.4)	978 (18.3)	11.8	Public
MOT_FILTER 18.	Anonymous submission
retrack 19.	31.5	50.2 ±13.5	52.1	18.4%	34.7%	6,324	83,910	615 (11.4)	2,086 (38.6)	22.3	Public
retrack 19.	Anonymous submission
TLO 20.	43.5	50.1 ±9.9	48.1	16.3%	40.7%	5,582	84,629	786 (14.7)	1,294 (24.1)	5.6	Public
TLO 20.	Anonymous submission
Tracker	Avg Rank	MOTA	IDF1	MT	ML	FP	FN	ID Sw.	Frag	Hz	Detector
MEN 21.	39.4	50.0 ±9.1	52.8	15.0%	37.0%	6,117	84,271	706 (13.1)	1,797 (33.4)	2.0	Public
MEN 21.	Anonymous submission
SCNet 22.	49.8	50.0 ±8.9	51.1	15.5%	34.1%	10,526	79,755	866 (15.4)	2,141 (38.1)	0.3	Public
SCNet 22.	Anonymous submission
pairwise16 23.	30.3	50.0 ±65.9	52.4	19.4%	38.7%	10,995	79,568	628 (11.1)	939 (16.7)	22.3	Public
pairwise16 23.	Anonymous submission
ENFT 24.	23.8	50.0 ±8.2	54.6	17.8%	41.1%	8,214	82,541	479 (8.8)	724 (13.2)	22.3	Public
ENFT 24.	Anonymous submission
RTT 25.	45.7	49.9 ±8.0	49.3	19.0%	32.8%	9,927	80,406	955 (17.1)	2,247 (40.2)	1.8	Public
RTT 25.	Anonymous submission
MMHT16 26.	40.1	49.9 ±9.8	47.3	16.2%	40.7%	6,110	84,455	823 (15.3)	1,289 (24.0)	12.4	Public
MMHT16 26.	Anonymous submission
OMHT16 27.	42.7	49.8 ±9.9	46.7	16.1%	40.4%	6,244	84,342	888 (16.5)	1,332 (24.8)	12.4	Public
OMHT16 27.	Anonymous submission
CMT16 28.	26.5	49.8 ±9.0	59.2	16.6%	43.6%	9,229	81,882	365 (6.6)	617 (11.2)	6.3	Public
CMT16 28.	#Submission: TIP-21190-2019
NOTA 29.	31.2	49.8 ±8.3	55.3	17.9%	37.7%	7,248	83,614	614 (11.3)	1,372 (25.3)	19.2	Public
NOTA 29.	L. Chen, H. Ai, R. Chen, Z. Zhuang. Aggregate Tracklet Appearance Features for Multi-Object Tracking. In IEEE Signal Processing Letters, 2019.
TLO16 30.	42.3	49.8 ±10.0	47.8	16.6%	40.6%	6,085	84,623	782 (14.6)	1,278 (23.8)	12.4	Public
TLO16 30.	Anonymous submission
Tracker	Avg Rank	MOTA	IDF1	MT	ML	FP	FN	ID Sw.	Frag	Hz	Detector
siameseCos 31.	38.7	49.4 ±8.4	49.8	19.1%	39.4%	6,281	85,384	679 (12.8)	823 (15.5)	0.8	Public
siameseCos 31.	In preparation
STCG 32.	34.5	49.3 ±8.6	52.0	16.2%	41.4%	6,886	84,979	515 (9.6)	775 (14.5)	22.3	Public
STCG 32.	Anonymous submission
HCC 33.	31.0	49.3 ±10.2	50.7	17.8%	39.9%	5,333	86,795	391 (7.5)	535 (10.2)	0.8	Public
HCC 33.	L. Ma, S. Tang, M. Black, L. Gool. Customized Multi-Person Tracker. In Computer Vision -- ACCV 2018, 2018.
LSST16O 34.	44.3	49.2 ±10.2	56.5	13.4%	41.4%	7,187	84,875	606 (11.3)	2,497 (46.7)	2.0	Public
LSST16O 34.	Anonymous submission
eTC 35.	34.8	49.2 ±9.1	56.1	17.3%	40.3%	8,400	83,702	606 (11.2)	882 (16.3)	0.7	Public
eTC 35.	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.
MOTHP 36.	35.8	49.1 ±9.1	46.9	20.0%	38.9%	9,038	83,031	679 (12.5)	850 (15.6)	11.8	Public
MOTHP 36.	Anonymous submission
AFN 37.	39.2	49.0 ±10.2	48.2	19.1%	35.7%	9,508	82,506	899 (16.4)	1,383 (25.3)	0.6	Public
AFN 37.	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 38.	32.3	49.0 ±7.2	53.9	18.1%	35.8%	8,495	83,838	621 (11.5)	1,252 (23.2)	1.5	Public
CRF_RNN16 38.	Anonymous submission
DAST 39.	38.5	48.9 ±8.4	53.2	15.2%	36.2%	9,987	82,427	838 (15.3)	1,936 (35.3)	8.7	Public
DAST 39.	Anonymous submission
KCF16 40.	46.9	48.8 ±9.6	47.2	15.8%	38.1%	5,875	86,567	906 (17.3)	1,116 (21.2)	0.1	Public
KCF16 40.	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
LMP 41.	35.4	48.8 ±9.8	51.3	18.2%	40.1%	6,654	86,245	481 (9.1)	595 (11.3)	0.5	Public
LMP 41.	S. Tang, M. Andriluka, B. Andres, B. Schiele. Multiple People Tracking with Lifted Multicut and Person Re-identification. In CVPR, 2017.
DeepMP16 42.	33.3	48.7 ±10.3	50.1	15.0%	43.6%	4,111	88,862	535 (10.4)	873 (17.0)	9.9	Public
DeepMP16 42.	Anonymous submission
TLMHT 43.	38.8	48.7 ±8.6	55.3	15.7%	44.5%	6,632	86,504	413 (7.9)	642 (12.2)	4.8	Public
TLMHT 43.	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.
STRN_MOT16 44.	43.5	48.5 ±8.5	53.9	17.0%	34.9%	9,038	84,178	747 (13.9)	2,919 (54.2)	13.5	Public
STRN_MOT16 44.	J. Xu, Y. Cao, Z. Zhang, H. Hu. Spatial-Temporal Relation Networks for Multi-Object Tracking. In ICCV, 2019.
MOTPPF 45.	35.0	48.4 ±8.8	48.5	19.1%	39.8%	9,152	84,266	595 (11.1)	802 (14.9)	11.8	Public
MOTPPF 45.	Anonymous submission
MOTPP 46.	39.5	48.3 ±8.7	45.4	18.6%	40.1%	7,378	86,181	661 (12.5)	834 (15.8)	11.8	Public
MOTPP 46.	Anonymous submission
AOReid 47.	39.9	48.2 ±8.7	50.8	15.3%	36.8%	10,283	83,301	821 (15.1)	1,963 (36.1)	11.2	Public
AOReid 47.	Anonymous submission
GCRA 48.	44.5	48.2 ±8.3	48.6	12.9%	41.1%	5,104	88,586	821 (16.0)	1,117 (21.7)	2.8	Public
GCRA 48.	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 49.	51.7	48.2 ±8.5	51.3	14.2%	36.8%	7,767	85,973	790 (14.9)	2,006 (38.0)	1.4	Public
SRPN16 49.	Anonymous submission
FWT 50.	47.8	47.8 ±9.4	44.3	19.1%	38.2%	8,886	85,487	852 (16.0)	1,534 (28.9)	0.6	Public
FWT 50.	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 51.	45.1	47.6 ±8.2	50.9	15.2%	38.3%	9,253	85,431	792 (14.9)	1,858 (35.0)	20.6	Public
MOTDT 51.	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 52.	38.8	47.6 ±10.6	47.3	17.0%	40.4%	5,844	89,093	629 (12.3)	768 (15.0)	8.3	Public
NLLMPa 52.	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 53.	34.4	47.4 ±10.4	50.1	17.3%	42.7%	8,369	86,931	575 (11.0)	913 (17.5)	197.3	Public
EAGS16 53.	H. Sheng, X. Zhang, Y. Zhang, Y. Wu, J. Chen. Enhanced Association with Supervoxels in Multiple Hypothesis Tracking. In IEEE Access, 2018.
JCSTD 54.	52.8	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
JCSTD 54.	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 55.	41.0	47.2 ±9.6	44.3	16.3%	41.6%	4,680	90,877	633 (12.6)	814 (16.2)	0.5	Public
ASTT 55.	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 56.	39.4	47.2 ±16.8	52.4	18.6%	42.8%	12,586	83,107	542 (10.0)	787 (14.5)	0.5	Public
eHAF16 56.	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 57.	47.1	47.2 ±7.7	46.3	14.0%	41.6%	2,681	92,856	774 (15.8)	1,675 (34.1)	1.0	Public
AMIR 57.	A. Sadeghian, A. Alahi, S. Savarese. Tracking The Untrackable: Learning To Track Multiple Cues with Long-Term Dependencies. In ICCV, 2017.
MCjoint 58.	38.3	47.1 ±10.8	52.3	20.4%	46.9%	6,703	89,368	370 (7.3)	598 (11.7)	0.6	Public
MCjoint 58.	}@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} }
YOONKJ16 59.	48.8	47.0 ±8.4	50.1	16.5%	41.8%	7,901	88,179	627 (12.1)	945 (18.3)	3.5	Public
YOONKJ16 59.	Anonymous submission
NOMT 60.	37.9	46.4 ±9.9	53.3	18.3%	41.4%	9,753	87,565	359 (6.9)	504 (9.7)	2.6	Public
NOMT 60.	W. Choi. Near-Online Multi-target Tracking with Aggregated Local Flow Descriptor. In ICCV, 2015.
Tracker	Avg Rank	MOTA	IDF1	MT	ML	FP	FN	ID Sw.	Frag	Hz	Detector
JMC 61.	46.3	46.3 ±9.0	46.3	15.5%	39.7%	6,373	90,914	657 (13.1)	1,114 (22.2)	0.8	Public
JMC 61.	S. Tang, B. Andres, M. Andriluka, B. Schiele. Multi-Person Tracking by Multicuts and Deep Matching. In BMTT, 2016.
DD_TAMA16 62.	40.7	46.2 ±8.4	49.4	14.1%	44.0%	5,126	92,367	598 (12.1)	1,127 (22.8)	6.5	Public
DD_TAMA16 62.	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.
DMAN 63.	44.5	46.1 ±11.1	54.8	17.4%	42.7%	7,909	89,874	532 (10.5)	1,616 (31.9)	0.3	Public
DMAN 63.	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 64.	55.1	46.0 ±9.1	50.0	14.6%	43.6%	6,895	91,117	473 (9.5)	1,422 (28.4)	0.2	Public
STAM16 64.	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 65.	44.1	46.0 ±8.2	46.5	15.5%	42.6%	5,124	92,697	693 (14.1)	759 (15.4)	0.7	Public
deepS2 65.	ID 32
RAR16pub 66.	56.5	45.9 ±9.7	48.8	13.2%	41.9%	6,871	91,173	648 (13.0)	1,992 (39.8)	0.9	Public
RAR16pub 66.	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 67.	47.1	45.8 ±8.9	46.1	16.2%	43.2%	6,412	91,758	590 (11.9)	781 (15.7)	0.8	Public
MHT_DAM 67.	C. Kim, F. Li, A. Ciptadi, J. Rehg. Multiple Hypothesis Tracking Revisited. In ICCV, 2015.
MTDF 68.	62.6	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
MTDF 68.	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.
INTERA_MOT 69.	42.3	45.4 ±8.6	47.7	18.1%	38.7%	13,407	85,547	600 (11.3)	930 (17.5)	4.3	Public
INTERA_MOT 69.	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 70.	45.3	45.3 ±9.1	47.9	17.0%	39.9%	11,122	87,890	639 (12.3)	946 (18.3)	1.8	Public
EDMT 70.	J. Chen, H. Sheng, Y. Zhang, Z. Xiong. Enhancing Detection Model for Multiple Hypothesis Tracking. In BMTT-PETS CVPRw, 2017.
Tracker	Avg Rank	MOTA	IDF1	MT	ML	FP	FN	ID Sw.	Frag	Hz	Detector
DCCRF16 71.	56.6	44.8 ±9.8	39.7	14.1%	42.3%	5,613	94,133	968 (20.0)	1,378 (28.5)	0.1	Public
DCCRF16 71.	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 72.	59.0	44.6 ±9.3	42.6	12.3%	43.9%	4,136	96,128	790 (16.7)	1,419 (30.0)	3.0	Public
TBSS 72.	X. Zhou, P. Jiang, Z. Wei, H. Dong, F. Wang. Online Multi-Object Tracking with Structural Invariance Constraint. In BMVC, 2018.
QuadMOT16 73.	57.8	44.1 ±9.4	38.3	14.6%	44.9%	6,388	94,775	745 (15.5)	1,096 (22.8)	1.8	Public
QuadMOT16 73.	J. Son, M. Baek, M. Cho, B. Han. Multi-Object Tracking with Quadruplet Convolutional Neural Networks. In CVPR, 2017.
SRPN 74.	60.8	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
SRPN 74.	Anonymous submission
CDA_DDALv2 75.	56.3	43.9 ±7.8	45.1	10.7%	44.4%	6,450	95,175	676 (14.1)	1,795 (37.6)	0.5	Public
CDA_DDALv2 75.	S. Bae and K. Yoon, Confidence-Based Data Association and Discriminative Deep Appearance Learning for Robust Online Multi-Object Tracking , In IEEE TPAMI, 2017.
LFNF16 76.	59.3	43.6 ±11.0	41.6	13.3%	45.7%	6,616	95,363	836 (17.5)	938 (19.7)	0.6	Public
LFNF16 76.	Sheng H, Hao L, Chen J, et al. Robust Local Effective Matching Model for Multi-Target Tracking. In PCM, 2017
oICF 77.	57.0	43.2 ±10.2	49.3	11.3%	48.5%	6,651	96,515	381 (8.1)	1,404 (29.8)	0.4	Public
oICF 77.	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.
AEb 78.	37.9	42.9 ±11.0	48.7	15.3%	49.0%	4,487	99,310	375 (8.2)	1,334 (29.3)	22.3	Public
AEb 78.	Anonymous submission
MHT_bLSTM6 79.	58.4	42.1 ±9.7	47.8	14.9%	44.4%	11,637	93,172	753 (15.4)	1,156 (23.6)	1.8	Public
MHT_bLSTM6 79.	C. Kim, F. Li, J. Rehg. Multi-object Tracking with Neural Gating Using Bilinear LSTM. In ECCV, 2018.
TestUnsup 80.	55.7	41.5 ±9.0	44.9	13.7%	43.5%	12,596	93,404	643 (13.2)	796 (16.3)	19.7	Public
TestUnsup 80.	Multi Object Tracking using Deep Structural Cost Minimization in Data Association
Tracker	Avg Rank	MOTA	IDF1	MT	ML	FP	FN	ID Sw.	Frag	Hz	Detector
OST16 81.	63.2	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
OST16 81.	Anonymous submission
LINF1 82.	54.3	41.0 ±9.5	45.7	11.6%	51.3%	7,896	99,224	430 (9.4)	963 (21.1)	4.2	Public
LINF1 82.	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 83.	64.4	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
PHD_GSDL16 83.	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.
PHD_T 84.	60.7	40.3 ±9.0	48.3	11.6%	43.1%	7,147	100,895	815 (18.2)	2,446 (54.8)	9.9	Public
PHD_T 84.	Anonymous submission
PMPTracker 85.	65.8	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
PMPTracker 85.	Light version of PTZ camera Mutiple People Tracker
AM_ADM 86.	59.9	40.1 ±10.1	43.8	7.1%	46.2%	8,503	99,891	789 (17.5)	1,736 (38.4)	5.8	Public
AM_ADM 86.	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 87.	52.1	39.9 ±9.1	35.3	8.7%	50.2%	1,133	107,586	790 (19.3)	824 (20.1)	8.5	Public
D_cost16 87.	Anonymous submission
SDMT 88.	56.8	39.6 ±8.3	42.3	11.7%	49.1%	11,130	98,343	602 (13.1)	772 (16.8)	19.8	Public
SDMT 88.	M. Thoreau, N. Kottege. Deep Similarity Metric Learning for Real-Time Pedestrian Tracking. In arXiv, 2018.
EAMTT_pub 89.	62.0	38.8 ±8.5	42.4	7.9%	49.1%	8,114	102,452	965 (22.0)	1,657 (37.8)	11.8	Public
EAMTT_pub 89.	R. Sanchez-Matilla, F. Poiesi, A. Cavallaro "Multi-target tracking with strong and weak detections" in BMTT ECCVw 2016
OVBT 90.	77.6	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
OVBT 90.	Y. Ban, S. Ba, X. Alameda-Pineda, R. Horaud. Tracking Multiple Persons Based on a Variational Bayesian Model. In BMTT 2016, .
Tracker	Avg Rank	MOTA	IDF1	MT	ML	FP	FN	ID Sw.	Frag	Hz	Detector
HAM_ACT16 91.	52.6	38.1 ±8.2	43.3	7.8%	54.4%	6,976	105,434	418 (9.9)	707 (16.8)	8.0	Public
HAM_ACT16 91.
GMMCP 92.	67.8	38.1 ±7.8	35.5	8.6%	50.9%	6,607	105,315	937 (22.2)	1,669 (39.5)	0.5	Public
GMMCP 92.	A. Dehghan, S. Assari, M. Shah.. GMMCP-Tracker:Globally Optimal Generalized Maximum Multi Clique Problem for Multiple Object Tracking. In CVPR, 2015.
LTTSC-CRF 93.	64.7	37.6 ±9.9	42.1	9.6%	55.2%	11,969	101,343	481 (10.8)	1,012 (22.8)	0.6	Public
LTTSC-CRF 93.	N. Le, A. Heili, M. Odobez. Long-Term Time-Sensitive Costs for CRF-Based Tracking by Detection. In ECCVw, 2016.
GoturnM16 94.	72.9	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
GoturnM16 94.	Anonymous submission
HISP_DAL 95.	67.3	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
HISP_DAL 95.	N. Baisa. Robust Online Multi-target Visual Tracking using a HISP Filter with Discriminative Deep Appearance Learning. In CoRR, 2019.
JCmin_MOT 96.	54.3	36.7 ±9.1	36.2	7.5%	54.4%	2,936	111,890	667 (17.3)	831 (21.5)	14.8	Public
JCmin_MOT 96.	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 97.	69.6	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
HISP_T 97.	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 98.	58.7	35.7 ±10.1	34.2	8.7%	50.7%	5,084	111,163	915 (23.4)	1,264 (32.4)	49.3	Public
LP2D 98.	MOT baseline: Linear programming on 2D image coordinates.
GM_PHD_DAL 99.	69.7	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
GM_PHD_DAL 99.	N. Baisa. Online Multi-object Visual Tracking using a GM-PHD Filter with Deep Appearance Learning. In 22nd International Conference on Information Fusion, 2019.
GM_PHD_Dl 100.	70.7	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
GM_PHD_Dl 100.	Anonymous submission
Tracker	Avg Rank	MOTA	IDF1	MT	ML	FP	FN	ID Sw.	Frag	Hz	Detector
RNN_A_P 101.	74.6	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
RNN_A_P 101.	Anonymous submission
GM_PHD_e17 102.	71.2	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
GM_PHD_e17 102.	Anonymous submission
TBD 103.	78.5	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
TBD 103.	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.
KVIOU16 104.	60.7	33.4 ±9.7	32.6	5.9%	59.6%	2,764	117,971	760 (21.5)	1,473 (41.7)	29.6	Public
KVIOU16 104.	Anonymous submission
GM_PHD_N1T 105.	69.0	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
GM_PHD_N1T 105.	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 106.	62.3	33.2 ±7.9	0.0	7.8%	54.4%	6,837	114,322	642 (17.2)	731 (19.6)	0.3	Public
CEM 106.	A. Milan, S. Roth, K. Schindler. Continuous Energy Minimization for Multitarget Tracking. In IEEE TPAMI, 2014.
CppSORT 107.	63.1	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
CppSORT 107.	S. Murray. Real-Time Multiple Object Tracking - A Study on the Importance of Speed. In arXiv preprint arXiv:1709.03572, 2017.
GMPHD_HDA 108.	54.7	30.5 ±6.9	33.4	4.6%	59.7%	5,169	120,970	539 (16.0)	731 (21.7)	13.6	Public
GMPHD_HDA 108.	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 109.	87.3	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
SMOT 109.	C. Dicle, O. Camps, M. Sznaier. The Way They Move: Tracking Targets with Similar Appearance. In ICCV, 2013.
DCOR 110.	63.2	28.3 ±9.0	21.7	3.4%	63.9%	1,618	128,345	849 (28.7)	2,592 (87.5)	32.9	Public
DCOR 110.	Anonymous submission
Tracker	Avg Rank	MOTA	IDF1	MT	ML	FP	FN	ID Sw.	Frag	Hz	Detector
MHT_ReID 111.	61.3	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
MHT_ReID 111.	Anonymous submission
JPDA_m 112.	54.6	26.2 ±6.1	0.0	4.1%	67.5%	3,689	130,549	365 (12.9)	638 (22.5)	22.2	Public
JPDA_m 112.	H. Rezatofighi, A. Milan, Z. Zhang, Q. Shi, A. Dick, I. Reid. Joint Probabilistic Data Association Revisited. In ICCV, 2015.
DP_NMS 113.	53.9	26.2 ±9.3	31.2	4.1%	67.5%	3,689	130,557	365 (12.9)	638 (22.5)	5.9	Public
DP_NMS 113.	H. Pirsiavash, D. Ramanan, C. Fowlkes. Globally-Optimal Greedy Algorithms for Tracking a Variable Number of Objects. In CVPR, 2011.
test_trker 114.	57.7	0.0 ±0.0	0.0	0.0%	100.0%	7	182,326	0 (nan)	0 (nan)	22.3	Public
test_trker 114.	Anonymous submission

Due to a minor bug in the export script, all results were re-evaluated on April 11, 2016. Here is the old snapshot of the leaderboard.

Benchmark Statistics

Sequences	Frames	Trajectories	Boxes
7	5919	759	182326

Difficulty Analysis

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

MOT16-03

(52.7% MOTA)

MOT16-06

(45.5% MOTA)

MOT16-07

(39.7% MOTA)

...

MOT16-08

(30.4% MOTA)

MOT16-14

(25.4% MOTA)

Evaluation Measures

Lower is better. Higher is better.

Measure	Better	Perfect	Description
Avg Rank	lower	1	This is the rank of each tracker averaged over all present evaluation measures.
MOTA	higher	100 %	Multiple Object Tracking Accuracy [1]. This measure combines three error sources: false positives, missed targets and identity switches.
MOTP	higher	100 %	Multiple Object Tracking Precision [1]. The misalignment between the annotated and the predicted bounding boxes.
IDF1	higher	100 %	ID F1 Score [2]. The ratio of correctly identified detections over the average number of ground-truth and computed detections.
FAF	lower	0	The average number of false alarms per frame.
MT	higher	100 %	Mostly tracked targets. The ratio of ground-truth trajectories that are covered by a track hypothesis for at least 80% of their respective life span.
ML	lower	0 %	Mostly lost targets. The ratio of ground-truth trajectories that are covered by a track hypothesis for at most 20% of their respective life span.
FP	lower	0	The total number of false positives.
FN	lower	0	The total number of false negatives (missed targets).
ID Sw.	lower	0	The total number of identity switches. Please note that we follow the stricter definition of identity switches as described in [3].
Frag	lower	0	The total number of times a trajectory is fragmented (i.e. interrupted during tracking).
Hz	higher	Inf.	Processing speed (in frames per second excluding the detector) on the benchmark.

Legend

Symbol	Description
	This is an online (causal) method, i.e. the solution is immediately available with each incoming frame and cannot be changed at any later time.
	This method used the provided detection set as input.
	This entry has been submitted or updated less than a week ago.

References:

[1]	Bernardin, K. & Stiefelhagen, R. Evaluating Multiple Object Tracking Performance: The CLEAR MOT Metrics. Image and Video Processing, 2008(1):1-10, 2008.
[2]	Ristani, E., Solera, F., Zou, R., Cucchiara, R. & Tomasi, C. Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking. In ECCV workshop on Benchmarking Multi-Target Tracking, 2016.
[3]	Li, Y., Huang, C. & Nevatia, R. Learning to associate: HybridBoosted multi-target tracker for crowded scene. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2009.