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
Response 1.	43.2	62.0 ±11.2	63.8	37.7%	20.7%	18,308	50,039	909 (12.5)	2,009 (27.7)	2.0	Public
	Anonymous submission
dpt_dpt 2.	26.5	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
TrkReid 3.	41.1	60.2 ±10.4	54.3	30.7%	20.2%	11,368	59,537	1,594 (23.7)	1,957 (29.1)	24.7	Public
	Anonymous submission
ISE_MOT16 4.	26.7	60.1 ±8.5	56.9	26.1%	29.1%	6,964	65,044	739 (11.5)	951 (14.8)	6.9	Public
	MIFT
DS_v2 5.	29.5	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
DpTrack 6.	34.1	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
SORT_Alex 7.	24.0	58.4 ±11.4	56.3	24.2%	32.9%	3,436	71,846	604 (10.0)	808 (13.3)	7.6	Public
	Anonymous submission
ReTrack16 8.	36.0	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
FFT16 9.	38.8	56.5 ±11.2	50.1	23.6%	29.4%	5,831	71,825	1,635 (27.0)	1,607 (26.5)	0.6	Public
	Anonymous submission
MHT___ReID 10.	45.3	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
Tracker	Avg Rank	MOTA	IDF1	MT	ML	FP	FN	ID Sw.	Frag	Hz	Detector
MPNTrack16 11.	24.4	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
MTT_TPR 12.	41.9	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
Tracktor16 13.	40.7	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.
retrack 14.	39.4	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
moT_ 15.	35.0	53.6 ±8.2	52.6	19.9%	32.3%	6,038	77,801	764 (13.3)	2,905 (50.7)	23.1	Public
	Anonymous submission
HDTR 16.	31.3	53.6 ±8.7	46.6	21.2%	37.0%	4,714	79,353	618 (10.9)	833 (14.7)	3.6	Public
TCT5 17.	50.8	51.9 ±13.9	51.8	25.4%	24.9%	15,622	70,557	1,494 (24.4)	2,466 (40.2)	4.7	Public
	Anonymous submission
TPM 18.	42.7	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
MOTHYPER 19.	35.9	50.9 ±9.1	47.4	19.4%	39.4%	4,866	84,022	619 (11.5)	814 (15.1)	4.8	Public
	Anonymous submission
UTA 20.	48.4	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
Tracker	Avg Rank	MOTA	IDF1	MT	ML	FP	FN	ID Sw.	Frag	Hz	Detector
MOTPP16 21.	39.4	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
PV 22.	53.0	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
TTL16 23.	51.2	50.4 ±10.3	50.1	17.4%	39.9%	8,491	81,156	807 (14.5)	1,251 (22.5)	6.7	Public
	Anonymous submission
MOTHPCLEAN 24.	37.8	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
CRF_TRACK 25.	37.0	50.3 ±7.9	54.4	18.3%	35.7%	7,148	82,746	702 (12.9)	1,387 (25.4)	1.5	Public
	Anonymous submission
CRFTrack16 26.	37.7	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
ENFT16 27.	34.2	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 28.	37.1	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
MOT_FILTER 29.	41.6	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
TLO 30.	52.0	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
Tracker	Avg Rank	MOTA	IDF1	MT	ML	FP	FN	ID Sw.	Frag	Hz	Detector
MEN 31.	47.2	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
SCNet 32.	59.0	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
pairwise16 33.	37.1	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
ENFT 34.	29.4	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
RTT 35.	55.1	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
MMHT16 36.	48.1	49.9 ±9.8	47.3	16.2%	40.7%	6,110	84,455	823 (15.3)	1,289 (24.0)	12.4	Public
	Anonymous submission
OMHT16 37.	51.0	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
CMT16 38.	32.3	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 39.	37.9	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.
TLO16 40.	50.8	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
Tracker	Avg Rank	MOTA	IDF1	MT	ML	FP	FN	ID Sw.	Frag	Hz	Detector
siameseCos 41.	46.8	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 42.	41.5	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 43.	37.6	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.
LSST16O 44.	52.3	49.2 ±10.2	56.5	13.4%	41.4%	7,187	84,875	606 (11.3)	2,497 (46.7)	2.0	Public
	Anonymous submission
eTC 45.	41.8	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 Proceedings of the 27th ACM International Conference on Multimedia, 2019.
MOTHP 46.	43.0	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
AFN 47.	47.4	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 48.	39.3	49.0 ±7.2	53.9	18.1%	35.8%	8,495	83,838	621 (11.5)	1,252 (23.2)	1.5	Public
	Anonymous submission
RFS 49.	60.5	49.0 ±14.5	50.3	15.3%	43.0%	9,953	82,394	701 (12.8)	1,663 (30.3)	1.0	Public
	Anonymous submission
DAST 50.	46.3	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
Tracker	Avg Rank	MOTA	IDF1	MT	ML	FP	FN	ID Sw.	Frag	Hz	Detector
KCF16 51.	55.6	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.
LMP 52.	42.8	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 53.	39.8	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
TLMHT 54.	46.3	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.
STRN_MOT16 55.	52.1	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.
MOTPPF 56.	42.5	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
MOTPP 57.	47.4	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
AOReid 58.	47.8	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
GCRA 59.	52.8	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 60.	61.1	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
Tracker	Avg Rank	MOTA	IDF1	MT	ML	FP	FN	ID Sw.	Frag	Hz	Detector
FWT 61.	56.8	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.
MOTDT 62.	52.9	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 63.	46.2	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.
WOLF 64.	64.2	47.5 ±8.5	41.9	14.8%	35.7%	10,939	83,428	1,276 (23.5)	2,480 (45.7)	31.3	Public
	Anonymous submission
CoCT16 65.	37.3	47.5 ±7.4	53.2	19.4%	38.7%	16,971	78,044	682 (11.9)	1,302 (22.8)	19.3	Public
	Anonymous submission
EAGS16 66.	40.9	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 67.	62.5	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
	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 68.	48.4	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.
eHAF16 69.	47.2	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 70.	55.3	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.
Tracker	Avg Rank	MOTA	IDF1	MT	ML	FP	FN	ID Sw.	Frag	Hz	Detector
MCjoint 71.	45.6	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} }
YOONKJ16 72.	57.8	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
NOMT 73.	45.3	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 74.	54.5	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.
DD_TAMA16 75.	48.1	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.
DMAN 76.	52.1	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 77.	64.3	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 78.	51.6	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 79.	65.8	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 80.	54.9	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
MTDF 81.	73.8	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.
INTERA_MOT 82.	50.3	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 83.	53.4	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 84.	65.7	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 85.	68.7	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.
OTCD_1 86.	64.6	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.
QuadMOT16 87.	67.4	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.
SRPN 88.	71.6	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
CDA_DDALv2 89.	65.4	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.
LFNF16 90.	69.2	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
oICF 91.	66.3	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.
AEb 92.	44.8	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
MHT_bLSTM6 93.	67.9	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.
TestUnsup 94.	64.7	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
OST16 95.	73.3	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 96.	63.2	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 97.	74.9	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.
PHD_T 98.	68.9	40.4 ±9.1	49.7	11.2%	43.3%	6,572	101,266	792 (17.8)	2,529 (56.9)	9.9	Public
	Anonymous submission
GMPHD_ReId 99.	61.9	40.4 ±9.3	49.7	11.2%	43.3%	6,572	101,266	792 (17.8)	2,529 (56.9)	31.6	Public
	N. Baisa. Occlusion-robust Online Multi-object Visual Tracking using a GM-PHD Filter with a CNN-based Re-identification. In , 2019.
PMPTracker 100.	76.1	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
AM_ADM 101.	69.5	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 102.	60.8	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
SDMT 103.	65.8	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.
EAMTT_pub 104.	71.8	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 105.	89.4	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, .
FTPLS 106.	70.1	38.2 ±9.2	47.5	9.6%	44.0%	18,915	93,051	689 (14.1)	2,006 (41.0)	5.1	Public
	Anonymous submission
HAM_ACT16 107.	61.0	38.1 ±8.2	43.3	7.8%	54.4%	6,976	105,434	418 (9.9)	707 (16.8)	8.0	Public
GMMCP 108.	78.6	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.
LTTSC-CRF 109.	74.8	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.
GoturnM16 110.	84.8	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
Tracker	Avg Rank	MOTA	IDF1	MT	ML	FP	FN	ID Sw.	Frag	Hz	Detector
HISP_DAL 111.	77.9	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.
JCmin_MOT 112.	62.8	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.
SCTrack_3L 113.	64.1	36.6 ±9.7	36.5	29.0%	15.8%	35,259	76,653	3,600 (62.1)	3,724 (64.3)	11.8	Public
	Anonymous submission
HISP_T 114.	80.5	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 115.	67.8	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.
GM_PHD_DAL 116.	81.0	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.
GM_PHD_Dl 117.	82.0	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 118.	86.4	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_e17 119.	82.5	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 120.	90.4	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.
Tracker	Avg Rank	MOTA	IDF1	MT	ML	FP	FN	ID Sw.	Frag	Hz	Detector
KVIOU16 121.	69.6	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
GM_PHD_N1T 122.	79.8	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 123.	71.5	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 124.	72.3	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 125.	62.8	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 126.	100.8	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.
DCOR 127.	72.4	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
MHT_ReID 128.	70.5	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
JPDA_m 129.	62.6	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 130.	62.0	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.
Tracker	Avg Rank	MOTA	IDF1	MT	ML	FP	FN	ID Sw.	Frag	Hz	Detector
test_trker 131.	66.3	0.0 ±0.0	0.0	0.0%	100.0%	7	182,326	0 (nan)	0 (nan)	22.3	Public
	Anonymous submission

Difficulty Analysis

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

MOT16-03

(53.9% MOTA)

MOT16-06

(46.2% MOTA)

MOT16-07

(40.8% MOTA)

...

...

MOT16-08

(31.0% MOTA)

MOT16-14

(26.3% MOTA)