MOT Challenge - Results

Click on a measure to sort the table accordingly. See below for a more detailed description.

Showing only entries that use public detections!

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

Tracker	MOTA	IDF1	MOTP	MT	ML	FP	FN	Recall	Precision	FAF	ID Sw.	Frag	Hz
DP_NMS 1.	26.2 ±9.7	31.2 ±5.1	76.3	31 (4.1)	512 (67.5)	3,689	130,557	28.4	93.3	0.6	365 (12.9)	638 (22.5)	212.6
DP_NMS 1.	H. Pirsiavash, D. Ramanan, C. Fowlkes. Globally-Optimal Greedy Algorithms for Tracking a Variable Number of Objects. In CVPR, 2011.
JPDA_m 2.	26.2 ±8.8	0.0 ±0.0	76.3	31 (4.1)	512 (67.5)	3,689	130,549	28.4	93.3	0.6	365 (12.9)	638 (22.5)	22.2
JPDA_m 2.	H. Rezatofighi, A. Milan, Z. Zhang, Q. Shi, A. Dick, I. Reid. Joint Probabilistic Data Association Revisited. In ICCV, 2015.
SMOT 3.	29.7 ±8.8	0.0 ±0.0	75.2	40 (5.3)	362 (47.7)	17,426	107,552	41.0	81.1	2.9	3,108 (75.8)	4,483 (109.3)	0.2
SMOT 3.	C. Dicle, O. Camps, M. Sznaier. The Way They Move: Tracking Targets with Similar Appearance. In ICCV, 2013.
GMPHD_HDA 4.	30.5 ±6.9	33.4 ±5.4	75.4	35 (4.6)	453 (59.7)	5,169	120,970	33.6	92.2	0.9	539 (16.0)	731 (21.7)	13.6
GMPHD_HDA 4.	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.
LM_NN 5.	31.0 ±7.2	31.5 ±8.3	78.4	56 (7.4)	443 (58.4)	2,451	122,649	32.7	96.1	0.4	678 (20.7)	666 (20.3)	3.0
LM_NN 5.	ID NEUCOM-D-18-03230
CppSORT 6.	31.5 ±9.0	27.7 ±9.7	77.3	33 (4.3)	455 (59.9)	3,048	120,278	34.0	95.3	0.5	1,587 (46.6)	2,239 (65.8)	687.1
CppSORT 6.	S. Murray. Real-Time Multiple Object Tracking - A Study on the Importance of Speed. In arXiv preprint arXiv:1709.03572, 2017.
CEM 7.	33.2 ±8.8	0.0 ±0.0	75.8	59 (7.8)	413 (54.4)	6,837	114,322	37.3	90.9	1.2	642 (17.2)	731 (19.6)	5,919.0
CEM 7.	A. Milan, S. Roth, K. Schindler. Continuous Energy Minimization for Multitarget Tracking. In IEEE TPAMI, 2014.
GM_PHD_N1T 8.	33.3 ±9.0	25.5 ±8.2	76.8	42 (5.5)	425 (56.0)	1,750	116,452	36.1	97.4	0.3	3,499 (96.8)	3,594 (99.5)	9.9
GM_PHD_N1T 8.	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.
TBD 9.	33.7 ±8.8	0.0 ±0.0	76.5	55 (7.2)	411 (54.2)	5,804	112,587	38.2	92.3	1.0	2,418 (63.2)	2,252 (58.9)	1.3
TBD 9.	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_DAL 10.	35.1 ±9.1	26.6 ±7.1	76.6	53 (7.0)	390 (51.4)	2,350	111,886	38.6	96.8	0.4	4,047 (104.8)	5,338 (138.2)	3.5
GM_PHD_DAL 10.	N. Baisa. Online Multi-object Visual Tracking using a GM-PHD Filter with Deep Appearance Learning. In 2019 22th International Conference on Information Fusion (FUSION), 2019.
Tracker	MOTA	IDF1	MOTP	MT	ML	FP	FN	Recall	Precision	FAF	ID Sw.	Frag	Hz
LP2D 11.	35.7 ±12.3	34.2 ±9.3	75.8	66 (8.7)	385 (50.7)	5,084	111,163	39.0	93.3	0.9	915 (23.4)	1,264 (32.4)	49.3
LP2D 11.	MOT baseline: Linear programming on 2D image coordinates.
HISP_T 12.	35.9 ±8.7	28.9 ±0.0	76.1	59 (7.8)	380 (50.1)	6,412	107,918	40.8	92.1	1.1	2,594 (63.6)	2,298 (56.3)	4.8
HISP_T 12.	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.
SCTrack_3L 13.	36.6 ±9.7	36.5 ±5.5	73.9	220 (29.0)	120 (15.8)	35,259	76,653	58.0	75.0	6.0	3,600 (62.1)	3,724 (64.3)	11.8
SCTrack_3L 13.	Anonymous submission
JCmin_MOT 14.	36.7 ±9.1	36.2 ±10.5	75.9	57 (7.5)	413 (54.4)	2,936	111,890	38.6	96.0	0.5	667 (17.3)	831 (21.5)	14.8
JCmin_MOT 14.	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 15.	37.4 ±8.8	30.5 ±6.8	76.3	58 (7.6)	386 (50.9)	3,222	108,865	40.3	95.8	0.5	2,101 (52.1)	2,151 (53.4)	3.3
HISP_DAL 15.	N. Baisa. Robust Online Multi-target Visual Tracking using a HISP Filter with Discriminative Deep Appearance Learning. In CoRR, 2019.
LTTSC-CRF 16.	37.6 ±8.0	42.1 ±6.6	75.9	73 (9.6)	419 (55.2)	11,969	101,343	44.4	87.1	2.0	481 (10.8)	1,012 (22.8)	0.6
LTTSC-CRF 16.	N. Le, A. Heili, M. Odobez. Long-Term Time-Sensitive Costs for CRF-Based Tracking by Detection. In ECCVw, 2016.
GMMCP 17.	38.1 ±7.8	35.5 ±4.5	75.8	65 (8.6)	386 (50.9)	6,607	105,315	42.2	92.1	1.1	937 (22.2)	1,669 (39.5)	0.5
GMMCP 17.	A. Dehghan, S. Assari, M. Shah.. GMMCP-Tracker:Globally Optimal Generalized Maximum Multi Clique Problem for Multiple Object Tracking. In CVPR, 2015.
OVBT 18.	38.4 ±8.6	37.8 ±5.5	75.4	57 (7.5)	359 (47.3)	11,517	99,463	45.4	87.8	1.9	1,321 (29.1)	2,140 (47.1)	0.3
OVBT 18.	Y. Ban, S. Ba, X. Alameda-Pineda, R. Horaud. Tracking Multiple Persons Based on a Variational Bayesian Model. In BMTT 2016, .
EAMTT_pub 19.	38.8 ±8.6	42.4 ±7.4	75.1	60 (7.9)	373 (49.1)	8,114	102,452	43.8	90.8	1.4	965 (22.0)	1,657 (37.8)	11.8
EAMTT_pub 19.	R. Sanchez-Matilla, F. Poiesi, A. Cavallaro "Multi-target tracking with strong and weak detections" in BMTT ECCVw 2016
SDMT 20.	39.6 ±8.1	42.3 ±6.6	75.5	89 (11.7)	373 (49.1)	11,130	98,343	46.1	88.3	1.9	602 (13.1)	772 (16.8)	19.8
SDMT 20.	M. Thoreau, N. Kottege. Deep Similarity Metric Learning for Real-Time Pedestrian Tracking. In arXiv, 2018.
Tracker	MOTA	IDF1	MOTP	MT	ML	FP	FN	Recall	Precision	FAF	ID Sw.	Frag	Hz
AM_ADM 21.	40.1 ±10.1	43.8 ±9.6	75.4	54 (7.1)	351 (46.2)	8,503	99,891	45.2	90.6	1.4	789 (17.5)	1,736 (38.4)	5.8
AM_ADM 21.	S. Lee, M. Kim, S. Bae, Learning Discriminative Appearance Models for Online Multi-Object Tracking with Appearance Discriminability Measures, In IEEE Access, 2018.
PMPTracker 22.	40.3 ±11.7	38.2 ±8.4	73.3	79 (10.4)	319 (42.0)	10,071	97,524	46.5	89.4	1.7	1,343 (28.9)	2,764 (59.4)	148.0
PMPTracker 22.	Light version of PTZ camera Mutiple People Tracker
GMPHD_ReId 23.	40.4 ±9.3	49.7 ±8.5	75.2	85 (11.2)	329 (43.3)	6,572	101,266	44.5	92.5	1.1	792 (17.8)	2,529 (56.9)	31.6
GMPHD_ReId 23.	N. Baisa. Occlusion-robust Online Multi-object Visual Tracking using a GM-PHD Filter with a CNN-based Re-identification. In , 2019.
PHD_GSDL16 24.	41.0 ±8.9	43.1 ±6.9	75.9	86 (11.3)	315 (41.5)	6,498	99,257	45.6	92.7	1.1	1,810 (39.7)	3,650 (80.1)	8.3
PHD_GSDL16 24.	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 25.	41.0 ±10.1	45.7 ±8.5	74.8	88 (11.6)	389 (51.3)	7,896	99,224	45.6	91.3	1.3	430 (9.4)	963 (21.1)	4.2
LINF1 25.	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.
TestUnsup 26.	41.5 ±9.0	44.9 ±8.8	75.2	104 (13.7)	330 (43.5)	12,596	93,404	48.8	87.6	2.1	643 (13.2)	796 (16.3)	19.7
TestUnsup 26.	Multi Object Tracking using Deep Structural Cost Minimization in Data Association
MHT_bLSTM6 27.	42.1 ±8.6	47.8 ±7.4	75.9	113 (14.9)	337 (44.4)	11,637	93,172	48.9	88.5	2.0	753 (15.4)	1,156 (23.6)	1.8
MHT_bLSTM6 27.	C. Kim, F. Li, J. Rehg. Multi-object Tracking with Neural Gating Using Bilinear LSTM. In ECCV, 2018.
oICF 28.	43.2 ±10.6	49.3 ±9.1	74.3	86 (11.3)	368 (48.5)	6,651	96,515	47.1	92.8	1.1	381 (8.1)	1,404 (29.8)	0.4
oICF 28.	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 29.	43.6 ±8.4	41.6 ±7.8	76.6	101 (13.3)	347 (45.7)	6,616	95,363	47.7	92.9	1.1	836 (17.5)	938 (19.7)	0.6
LFNF16 29.	Sheng H, Hao L, Chen J, et al. Robust Local Effective Matching Model for Multi-Target Tracking. In PCM, 2017
CDA_DDALv2 30.	43.9 ±7.8	45.1 ±5.7	74.7	81 (10.7)	337 (44.4)	6,450	95,175	47.8	93.1	1.1	676 (14.1)	1,795 (37.6)	0.5
CDA_DDALv2 30.	S. Bae and K. Yoon, Confidence-Based Data Association and Discriminative Deep Appearance Learning for Robust Online Multi-Object Tracking , In IEEE TPAMI, 2017.
Tracker	MOTA	IDF1	MOTP	MT	ML	FP	FN	Recall	Precision	FAF	ID Sw.	Frag	Hz
QuadMOT16 31.	44.1 ±9.4	38.3 ±8.6	76.4	111 (14.6)	341 (44.9)	6,388	94,775	48.0	93.2	1.1	745 (15.5)	1,096 (22.8)	1.8
QuadMOT16 31.	J. Son, M. Baek, M. Cho, B. Han. Multi-Object Tracking with Quadruplet Convolutional Neural Networks. In CVPR, 2017.
OTCD_1 32.	44.4 ±10.8	45.6 ±10.4	75.4	88 (11.6)	361 (47.6)	5,759	94,927	47.9	93.8	1.0	759 (15.8)	1,787 (37.3)	17.6
OTCD_1 32.	Q. Liu, B. Liu, Y. Wu, W. Li, N. Yu. Real-Time Online Multi-Object Tracking in Compressed Domain. In IEEE Access, 2019.
TBSS 33.	44.6 ±9.3	42.6 ±7.2	75.2	93 (12.3)	333 (43.9)	4,136	96,128	47.3	95.4	0.7	790 (16.7)	1,419 (30.0)	3.0
TBSS 33.	X. Zhou, P. Jiang, Z. Wei, H. Dong, F. Wang. Online Multi-Object Tracking with Structural Invariance Constraint. In BMVC, 2018.
DCCRF16 34.	44.8 ±9.5	39.7 ±8.2	75.6	107 (14.1)	321 (42.3)	5,613	94,133	48.4	94.0	0.9	968 (20.0)	1,378 (28.5)	0.1
DCCRF16 34.	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 35.	45.3 ±9.1	47.9 ±7.8	75.9	129 (17.0)	303 (39.9)	11,122	87,890	51.8	89.5	1.9	639 (12.3)	946 (18.3)	1.8
EDMT 35.	J. Chen, H. Sheng, Y. Zhang, Z. Xiong. Enhancing Detection Model for Multiple Hypothesis Tracking. In BMTT-PETS CVPRw, 2017.
INTERA_MOT 36.	45.4 ±8.1	47.7 ±9.0	74.4	137 (18.1)	294 (38.7)	13,407	85,547	53.1	87.8	2.3	600 (11.3)	930 (17.5)	4.3
INTERA_MOT 36.	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 37.	45.7 ±10.8	40.1 ±8.7	72.6	107 (14.1)	276 (36.4)	12,018	84,970	53.4	89.0	2.0	1,987 (37.2)	3,377 (63.2)	1.5
MTDF 37.	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 38.	45.8 ±8.6	46.1 ±7.6	76.3	123 (16.2)	328 (43.2)	6,412	91,758	49.7	93.4	1.1	590 (11.9)	781 (15.7)	0.8
MHT_DAM 38.	C. Kim, F. Li, A. Ciptadi, J. Rehg. Multiple Hypothesis Tracking Revisited. In ICCV, 2015.
RAR16pub 39.	45.9 ±9.7	48.8 ±7.6	74.8	100 (13.2)	318 (41.9)	6,871	91,173	50.0	93.0	1.2	648 (13.0)	1,992 (39.8)	0.9
RAR16pub 39.	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 40.	46.0 ±8.2	46.5 ±6.1	76.3	118 (15.5)	323 (42.6)	5,124	92,697	49.2	94.6	0.9	693 (14.1)	759 (15.4)	0.7
deepS2 40.	ID 32
Tracker	MOTA	IDF1	MOTP	MT	ML	FP	FN	Recall	Precision	FAF	ID Sw.	Frag	Hz
STAM16 41.	46.0 ±9.1	50.0 ±8.4	74.9	111 (14.6)	331 (43.6)	6,895	91,117	50.0	93.0	1.2	473 (9.5)	1,422 (28.4)	0.2
STAM16 41.	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 42.	46.1 ±9.1	54.8 ±7.0	73.8	132 (17.4)	324 (42.7)	7,909	89,874	50.7	92.1	1.3	532 (10.5)	1,616 (31.9)	0.3
DMAN 42.	J. Zhu, H. Yang, N. Liu, M. Kim, W. Zhang, M. Yang. Online Multi-Object Tracking with Dual Matching Attention Networks. In ECCV, 2018.
DASOT16 43.	46.1 ±9.2	49.4 ±8.8	75.3	111 (14.6)	316 (41.6)	8,222	89,204	51.1	91.9	1.4	802 (15.7)	2,057 (40.3)	9.0
DASOT16 43.	Q. Chu, W. Ouyang, B. Liu, F. Zhu, N. Yu. DASOT: A Unified Framework Integrating Data Association and Single Object Tracking for Online Multi-Object Tracking. In Proceedings of the AAAI Conference on Artificial Intelligence, 2020.
DD_TAMA16 44.	46.2 ±8.4	49.4 ±7.6	75.4	107 (14.1)	334 (44.0)	5,126	92,367	49.3	94.6	0.9	598 (12.1)	1,127 (22.8)	6.5
DD_TAMA16 44.	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 45.	46.3 ±9.4	46.3 ±9.2	75.7	118 (15.5)	301 (39.7)	6,373	90,914	50.1	93.5	1.1	657 (13.1)	1,114 (22.2)	0.8
JMC 45.	S. Tang, B. Andres, M. Andriluka, B. Schiele. Multi-Person Tracking by Multicuts and Deep Matching. In BMTT, 2016.
NOMT 46.	46.4 ±8.9	53.3 ±7.5	76.6	139 (18.3)	314 (41.4)	9,753	87,565	52.0	90.7	1.6	359 (6.9)	504 (9.7)	2.6
NOMT 46.	W. Choi. Near-Online Multi-target Tracking with Aggregated Local Flow Descriptor. In ICCV, 2015.
YOONKJ16 47.	47.0 ±8.1	50.1 ±7.5	75.8	125 (16.5)	317 (41.8)	7,901	88,179	51.6	92.3	1.3	627 (12.1)	945 (18.3)	3.5
YOONKJ16 47.	K. YOON, J. GWAK, Y. SONG, Y. YOON, M. JEON. OneShotDA: Online Multi-object Tracker with One-shot-learning-based Data Association. In IEEE Access, 2020.
MCjoint 48.	47.1 ±10.3	52.3 ±10.2	76.3	155 (20.4)	356 (46.9)	6,703	89,368	51.0	93.3	1.1	370 (7.3)	598 (11.7)	0.6
MCjoint 48.	}@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 49.	47.2 ±8.2	46.3 ±7.2	75.8	106 (14.0)	316 (41.6)	2,681	92,856	49.1	97.1	0.5	774 (15.8)	1,675 (34.1)	1.0
AMIR 49.	A. Sadeghian, A. Alahi, S. Savarese. Tracking The Untrackable: Learning To Track Multiple Cues with Long-Term Dependencies. In ICCV, 2017.
eHAF16 50.	47.2 ±8.7	52.4 ±7.6	75.7	141 (18.6)	325 (42.8)	12,586	83,107	54.4	88.7	2.1	542 (10.0)	787 (14.5)	0.5
eHAF16 50.	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.
Tracker	MOTA	IDF1	MOTP	MT	ML	FP	FN	Recall	Precision	FAF	ID Sw.	Frag	Hz
ASTT 51.	47.2 ±9.6	44.3 ±8.5	76.1	124 (16.3)	316 (41.6)	4,680	90,877	50.2	95.1	0.8	633 (12.6)	814 (16.2)	0.5
ASTT 51.	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 52.	47.4 ±8.3	41.1 ±5.6	74.4	109 (14.4)	276 (36.4)	8,076	86,638	52.5	92.2	1.4	1,266 (24.1)	2,697 (51.4)	8.8
JCSTD 52.	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 53.	47.4 ±8.6	50.1 ±7.4	75.9	131 (17.3)	324 (42.7)	8,369	86,931	52.3	91.9	1.4	575 (11.0)	913 (17.5)	197.3
EAGS16 53.	H. Sheng, X. Zhang, Y. Zhang, Y. Wu, J. Chen. Enhanced Association with Supervoxels in Multiple Hypothesis Tracking. In IEEE Access, 2018.
NLLMPa 54.	47.6 ±10.6	47.3 ±9.6	78.5	129 (17.0)	307 (40.4)	5,844	89,093	51.1	94.1	1.0	629 (12.3)	768 (15.0)	8.3
NLLMPa 54.	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 55.	47.6 ±8.4	50.9 ±5.5	74.8	115 (15.2)	291 (38.3)	9,253	85,431	53.1	91.3	1.6	792 (14.9)	1,858 (35.0)	20.6
MOTDT 55.	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 56.	47.8 ±10.0	44.3 ±11.0	75.5	145 (19.1)	290 (38.2)	8,886	85,487	53.1	91.6	1.5	852 (16.0)	1,534 (28.9)	0.6
FWT 56.	R. Henschel, L. Leal-Taixé, D. Cremers, B. Rosenhahn. Fusion of Head and Full-Body Detectors for Multi-Object Tracking. In Trajnet CVPRW, 2018.
GCRA 57.	48.2 ±8.3	48.6 ±5.6	77.5	98 (12.9)	312 (41.1)	5,104	88,586	51.4	94.8	0.9	821 (16.0)	1,117 (21.7)	2.8
GCRA 57.	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.
STRN_MOT16 58.	48.5 ±8.5	53.9 ±7.1	73.7	129 (17.0)	265 (34.9)	9,038	84,178	53.8	91.6	1.5	747 (13.9)	2,919 (54.2)	13.5
STRN_MOT16 58.	J. Xu, Y. Cao, Z. Zhang, H. Hu. Spatial-Temporal Relation Networks for Multi-Object Tracking. In ICCV, 2019.
TLMHT 59.	48.7 ±8.6	55.3 ±5.6	76.4	119 (15.7)	338 (44.5)	6,632	86,504	52.6	93.5	1.1	413 (7.9)	642 (12.2)	4.8
TLMHT 59.	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.
LMP 60.	48.8 ±8.9	51.3 ±6.8	79.0	138 (18.2)	304 (40.1)	6,654	86,245	52.7	93.5	1.1	481 (9.1)	595 (11.3)	0.5
LMP 60.	S. Tang, M. Andriluka, B. Andres, B. Schiele. Multiple People Tracking with Lifted Multicut and Person Re-identification. In CVPR, 2017.
Tracker	MOTA	IDF1	MOTP	MT	ML	FP	FN	Recall	Precision	FAF	ID Sw.	Frag	Hz
KCF16 61.	48.8 ±9.6	47.2 ±7.8	75.7	120 (15.8)	289 (38.1)	5,875	86,567	52.5	94.2	1.0	906 (17.3)	1,116 (21.2)	0.1
KCF16 61.	P. Chu, H. Fan, C. Tan, H. Ling. Online Multi-Object Tracking with Instance-Aware Tracker and Dynamic Model Refreshment. In WACV, 2019.
DAST 62.	48.9 ±8.5	53.2 ±5.5	74.9	115 (15.2)	275 (36.2)	9,987	82,427	54.8	90.9	1.7	838 (15.3)	1,936 (35.3)	8.7
DAST 62.	Anonymous submission
AFN 63.	49.0 ±10.2	48.2 ±7.4	78.0	145 (19.1)	271 (35.7)	9,508	82,506	54.7	91.3	1.6	899 (16.4)	1,383 (25.3)	0.6
AFN 63.	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.
eTC 64.	49.2 ±9.1	56.1 ±7.2	75.5	131 (17.3)	306 (40.3)	8,400	83,702	54.1	92.2	1.4	606 (11.2)	882 (16.3)	0.7
eTC 64.	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 65.	49.2 ±10.2	56.5 ±7.2	74.0	102 (13.4)	314 (41.4)	7,187	84,875	53.4	93.1	1.2	606 (11.3)	2,497 (46.7)	2.0
LSST16O 65.	Multi-Object Tracking with Multiple Cues and Switcher-Aware Classification
HCC 66.	49.3 ±10.2	50.7 ±7.4	79.0	135 (17.8)	303 (39.9)	5,333	86,795	52.4	94.7	0.9	391 (7.5)	535 (10.2)	0.8
HCC 66.	L. Ma, S. Tang, M. Black, L. Gool. Customized Multi-Person Tracker. In Computer Vision -- ACCV 2018, 2018.
S_H_T 67.	49.3 ±10.8	50.7 ±7.1	77.5	158 (20.8)	242 (31.9)	16,998	74,142	59.3	86.4	2.9	1,302 (21.9)	2,183 (36.8)	0.9
S_H_T 67.	Anonymous submission
siameseCos 68.	49.4 ±8.1	49.8 ±7.4	75.9	145 (19.1)	299 (39.4)	6,281	85,384	53.2	93.9	1.1	679 (12.8)	823 (15.5)	0.8
siameseCos 68.	In preparation
Seq2Seq 69.	49.8 ±9.5	44.6 ±8.0	77.0	114 (15.0)	308 (40.6)	2,835	87,813	51.8	97.1	0.5	868 (16.7)	1,093 (21.1)	2.9
Seq2Seq 69.	Anonymous submission
CMT16 70.	49.8 ±9.0	59.2 ±6.5	76.1	126 (16.6)	331 (43.6)	9,229	81,882	55.1	91.6	1.6	365 (6.6)	617 (11.2)	6.3
CMT16 70.	#Submission: TIP-21190-2019
Tracker	MOTA	IDF1	MOTP	MT	ML	FP	FN	Recall	Precision	FAF	ID Sw.	Frag	Hz
NOTA 71.	49.8 ±8.3	55.3 ±5.4	74.5	136 (17.9)	286 (37.7)	7,248	83,614	54.1	93.2	1.2	614 (11.3)	1,372 (25.3)	19.2
NOTA 71.	L. Chen, H. Ai, R. Chen, Z. Zhuang. Aggregate Tracklet Appearance Features for Multi-Object Tracking. In IEEE Signal Processing Letters, 2019.
ENFT16 72.	50.3 ±8.2	55.0 ±6.4	76.2	146 (19.2)	302 (39.8)	8,341	81,843	55.1	92.3	1.4	490 (8.9)	754 (13.7)	0.4
ENFT16 72.	BUAA
CRF_TRACK 73.	50.3 ±7.9	54.4 ±6.4	74.8	139 (18.3)	271 (35.7)	7,148	82,746	54.6	93.3	1.2	702 (12.9)	1,387 (25.4)	1.5
CRF_TRACK 73.	Jun xiang, Chao Ma, Guohan Xu, Jianhua Hou, End-to-End Learning Deep CRF models for Multi-Object Tracking. In IEEE Transactions on Circuits and Systems for Video Technology, 2020
PV 74.	50.4 ±10.3	50.8 ±7.7	77.7	113 (14.9)	295 (38.9)	2,600	86,780	52.4	97.4	0.4	1,061 (20.2)	3,181 (60.7)	7.3
PV 74.	X. S. Li, Y. T. Liu, K. F. Wang. Multi-Target Tracking with Trajectory Prediction and Re-Identification//2019 Chinese Automation Congress. IEEE.
MLT 75.	52.8 ±8.2	62.6 ±6.8	76.1	160 (21.1)	322 (42.4)	5,362	80,444	55.9	95.0	0.9	299 (5.4)	702 (12.6)	5.9
MLT 75.	Y. Zhang, H. Sheng, Y. Wu, S. Wang, W. Ke, Z. Xiong. Multiplex Labeling Graph for Near Online Tracking in Crowded Scenes. In IEEE Internet of Things Journal, 2020.
HDTR 76.	53.6 ±8.7	46.6 ±6.8	80.8	161 (21.2)	281 (37.0)	4,714	79,353	56.5	95.6	0.8	618 (10.9)	833 (14.7)	3.6
HDTR 76.	M. Babaee, A. Athar, G. Rigoll. Multiple People Tracking Using Hierarchical Deep Tracklet Re-identification. In arXiv preprint arXiv:1811.04091, 2018.
GNN_tracktor 77.	53.9 ±11.1	53.7 ±10.6	77.0	127 (16.7)	299 (39.4)	2,514	80,892	55.6	97.6	0.4	631 (11.3)	894 (16.1)	2.2
GNN_tracktor 77.	Anonymous submission
Tracktor++ 78.	54.4 ±12.0	52.5 ±9.6	78.2	144 (19.0)	280 (36.9)	3,280	79,149	56.6	96.9	0.6	682 (12.1)	1,480 (26.2)	1.5
Tracktor++ 78.	P. Bergmann, T. Meinhardt, L. Leal-Taixé. Tracking without bells and whistles. In ICCV, 2019.
TARCA 79.	54.8 ±12.5	57.3 ±8.9	77.4	175 (23.1)	291 (38.3)	6,679	75,244	58.7	94.1	1.1	487 (8.3)	828 (14.1)	7.6
TARCA 79.	Anonymous submission
TrctrD16 80.	54.8 ±11.8	53.4 ±9.1	77.5	145 (19.1)	281 (37.0)	2,955	78,765	56.8	97.2	0.5	645 (11.4)	1,515 (26.7)	1.6
TrctrD16 80.	Y. Xu, A. Osep, Y. Ban, R. Horaud, L. Leal-Taixe, X. Alameda-Pineda. How To Train Your Deep Multi-Object Tracker. In , 2019.
Tracker	MOTA	IDF1	MOTP	MT	ML	FP	FN	Recall	Precision	FAF	ID Sw.	Frag	Hz
TrajTrack 81.	56.1 ±11.2	56.9 ±9.0	79.0	163 (21.5)	269 (35.4)	3,691	75,628	58.5	96.7	0.6	742 (12.7)	1,009 (17.2)	0.5
TrajTrack 81.	Anonymous submission
Tracktor++v2 82.	56.2 ±11.4	54.9 ±9.9	79.2	157 (20.7)	272 (35.8)	2,394	76,844	57.9	97.8	0.4	617 (10.7)	1,068 (18.5)	1.6
Tracktor++v2 82.	P. Bergmann, T. Meinhardt, L. Leal-Taixé. Tracking without bells and whistles. In ICCV, 2019.
M_track 83.	56.4 ±11.9	54.6 ±11.4	73.5	168 (22.1)	248 (32.7)	4,694	73,593	59.6	95.9	0.8	1,139 (19.1)	1,411 (23.7)	4.7
M_track 83.	Anonymous submission
VAN 84.	57.3 ±10.8	57.5 ±8.9	79.1	188 (24.8)	257 (33.9)	3,845	73,489	59.7	96.6	0.6	550 (9.2)	772 (12.9)	4.7
VAN 84.	Anonymous submission
M_track 85.	57.8 ±11.7	57.5 ±10.3	78.6	197 (26.0)	238 (31.4)	4,193	72,224	60.4	96.3	0.7	561 (9.3)	844 (14.0)	4.4
M_track 85.	Anonymous submission
MPNTrack 86.	58.6 ±10.3	61.7 ±7.3	78.9	207 (27.3)	258 (34.0)	4,949	70,252	61.5	95.8	0.8	354 (5.8)	684 (11.1)	6.5
MPNTrack 86.	G. Brasó, L. Leal-Taixé. Learning a Neural Solver for Multiple Object Tracking. In CVPR, 2020.
DN_MOT 87.	58.8 ±9.3	60.8 ±7.7	74.8	233 (30.7)	139 (18.3)	18,208	55,366	69.6	87.5	3.1	1,626 (23.4)	2,904 (41.7)	22.3
DN_MOT 87.	Anonymous submission
msot 88.	59.5 ±9.0	52.0 ±8.1	78.1	187 (24.6)	224 (29.5)	5,070	68,001	62.7	95.8	0.9	800 (12.8)	1,014 (16.2)	1.1
msot 88.	Anonymous submission
ISE_MOT16 89.	60.1 ±9.2	56.9 ±7.5	77.6	198 (26.1)	221 (29.1)	6,964	65,044	64.3	94.4	1.2	739 (11.5)	951 (14.8)	6.9
ISE_MOT16 89.	MIFT
Lif_T 90.	61.3 ±0.0	64.7 ±0.0	78.3	205 (27.0)	258 (34.0)	4,844	65,401	64.1	96.0	0.8	389 (6.1)	1,034 (16.1)	0.5
Lif_T 90.	Anonymous submission
Tracker	MOTA	IDF1	MOTP	MT	ML	FP	FN	Recall	Precision	FAF	ID Sw.	Frag	Hz
UnsupTrack 91.	62.4 ±14.6	58.5 ±9.8	78.3	205 (27.0)	242 (31.9)	5,909	61,981	66.0	95.3	1.0	588 (8.9)	1,361 (20.6)	1.9
UnsupTrack 91.	Simple Unsupervised Multi-Object Tracking (Under Review)

Sequences	Frames	Trajectories	Boxes
7	5919	759	182326

Difficulty Analysis

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

MOT16-03

(53.3 MOTA)

MOT16-06

(45.0 MOTA)

MOT16-12

(40.4 MOTA)

...

MOT16-08

(31.4 MOTA)

MOT16-14

(24.5 MOTA)

Evaluation Measures

Lower is better. Higher is better.

Measure	Better	Perfect	Description
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. The frequency is provided by the authors and not officially evaluated by the MOTChallenge.

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 method used the provided detection set as input.
	This entry has been submitted or updated less than a week ago.
	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.

MOT16 Results

Download all results for this benchmark

Benchmark Statistics

Difficulty Analysis

Evaluation Measures

Legend

References: