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
DS_v2 1.	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
DpTrack 2.	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
ReTrack16 3.	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
MHT___ReID 4.	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
MTT_TPR 5.	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
Tracktor16 6.	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.
HDTR 7.	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
TPM 8.	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
UTA 9.	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
MOTPP16 10.	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
Tracker	Avg Rank	MOTA	IDF1	MT	ML	FP	FN	ID Sw.	Frag	Hz	Detector
PV 11.	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
MOTHPCLEAN 12.	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
CRF_TRACK 13.	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 14.	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
ENFT16 15.	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
MOT_FILTER 16.	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
MEN 17.	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
pairwise16 18.	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
ENFT 19.	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
RTT 20.	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
Tracker	Avg Rank	MOTA	IDF1	MT	ML	FP	FN	ID Sw.	Frag	Hz	Detector
CMT16 21.	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 22.	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.
siameseCos 23.	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
STCG 24.	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
HCC 25.	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.
LSST16O 26.	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
eTC 27.	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.
MOTHP 28.	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
AFN 29.	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.
CRF_RNN16 30.	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
Tracker	Avg Rank	MOTA	IDF1	MT	ML	FP	FN	ID Sw.	Frag	Hz	Detector
DAST 31.	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
SCNet 32.	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
KCF16 33.	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.
LMP 34.	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.
DeepMP16 35.	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
TLMHT 36.	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.
MOTPPF 37.	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
MOTPP 38.	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
AOReid 39.	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
GCRA 40.	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.
Tracker	Avg Rank	MOTA	IDF1	MT	ML	FP	FN	ID Sw.	Frag	Hz	Detector
SRPN16 41.	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
FWT 42.	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.
MOTDT 43.	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.
NLLMPa 44.	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.
EAGS16 45.	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.
JCSTD 46.	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.
ASTT 47.	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.
eHAF16 48.	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.
AMIR 49.	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.
MCjoint 50.	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} }
Tracker	Avg Rank	MOTA	IDF1	MT	ML	FP	FN	ID Sw.	Frag	Hz	Detector
YOONKJ16 51.	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
NOMT 52.	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.
JMC 53.	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.
DD_TAMA16 54.	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.
DMAN 55.	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.
STAM16 56.	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.
deepS2 57.	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
RAR16pub 58.	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.
MHT_DAM 59.	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.
MTDF 60.	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.
Tracker	Avg Rank	MOTA	IDF1	MT	ML	FP	FN	ID Sw.	Frag	Hz	Detector
INTERA_MOT 61.	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.
EDMT 62.	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.
DCCRF16 63.	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.
TBSS 64.	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.
QuadMOT16 65.	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.
CDA_DDALv2 66.	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.
LFNF16 67.	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
oICF 68.	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.
MHT_bLSTM6 69.	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.
TestUnsup 70.	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
Tracker	Avg Rank	MOTA	IDF1	MT	ML	FP	FN	ID Sw.	Frag	Hz	Detector
OST16 71.	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
LINF1 72.	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.
PHD_GSDL16 73.	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.
PMPTracker 74.	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
AM_ADM 75.	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.
D_cost16 76.	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
EAMTT_pub 77.	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
OVBT 78.	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, .
HAM_ACT16 79.	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
GMMCP 80.	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.
Tracker	Avg Rank	MOTA	IDF1	MT	ML	FP	FN	ID Sw.	Frag	Hz	Detector
LTTSC-CRF 81.	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.
HISP_DAL 82.	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.
PHD_T 83.	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
JCmin_MOT 84.	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.
HISP_T 85.	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.
LP2D 86.	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.
GM_PHD_DAL 87.	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.
GM_PHD_Dl 88.	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
RNN_A_P 89.	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_e17 90.	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
Tracker	Avg Rank	MOTA	IDF1	MT	ML	FP	FN	ID Sw.	Frag	Hz	Detector
TBD 91.	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_N1T 92.	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.
CEM 93.	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.
CppSORT 94.	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.
GMPHD_HDA 95.	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.
SMOT 96.	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.
DCOR 97.	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
MHT_ReID 98.	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
retrack 99.	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
JPDA_m 100.	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.
Tracker	Avg Rank	MOTA	IDF1	MT	ML	FP	FN	ID Sw.	Frag	Hz	Detector
DP_NMS 101.	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.
test_trker 102.	51.7	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

(51.9% MOTA)

MOT16-06

(45.0% MOTA)

MOT16-07

(38.9% MOTA)

...

...

MOT16-08

(30.0% MOTA)

MOT16-14

(24.8% MOTA)