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!

Benchmark Statistics

Tracker	MOTA	IDF1	HOTA	MT	ML	FP	FN	Rcll	Prcn	AssA	DetA	AssRe	AssPr	DetRe	DetPr	LocA	FAF	ID Sw.	Frag	Hz
FLWM 1.	80.5	79.9	64.9	1,341 (56.9)	335 (14.2)	27,245	81,653	85.5	94.7	65.1	64.9	71.5	78.0	70.6	78.1	83.1	1.5	1,370 (0.0)	2,071 (0.0)	8.9
FLWM 1.	H. Liu, Y. Zhao, P. Dong, X. Guo, Y. Wang. IOF-Tracker: A Two-Stage Multiple Targets Tracking Method Using Spatial-Temporal Fusion Algorithm. In Applied Sciences, 2025.
FeatureSORT 2.	79.6	77.2	63.0	1,264 (53.7)	348 (14.8)	29,588	83,132	85.3	94.2	62.0	64.4	68.2	76.0	70.4	77.7	83.1	1.7	2,269 (0.0)	2,148 (0.0)	8.7
FeatureSORT 2.	H. Hashempoor, R. Koikara, Y. Hwang. FeatureSORT: Essential Features for Effective Tracking. In , 2024.
ISC 3.	79.1	78.7	63.6	1,083 (46.0)	396 (16.8)	17,190	99,555	82.4	96.4	63.6	63.8	69.0	79.6	68.2	79.9	83.5	1.0	1,014 (0.0)	2,247 (0.0)	88.8
ISC 3.
PermaTrack 4.	73.1	67.2	54.2	996 (42.3)	450 (19.1)	24,577	123,508	78.1	94.7	51.2	58.0	59.6	67.9	62.8	76.1	81.6	1.4	3,571 (0.0)	5,826 (0.0)	11.9
PermaTrack 4.	P. Tokmakov, J. Li, W. Burgard, A. Gaidon. Learning to Track with Object Permanence. In ICCV, 2021.
MOTer 5.	71.9	62.3	54.1	894 (38.0)	534 (22.7)	17,378	137,008	75.7	96.1	49.9	59.0	54.3	76.0	63.0	80.0	83.7	1.0	4,046 (0.0)	7,929 (0.0)	1.0
MOTer 5.	Y. Xu, Y. Ban, G. Delorme, C. Gan, D. Rus, X. Alameda-Pineda. TransCenter: Transformers with Dense Queries for Multiple-Object Tracking. In arXiv, 2021.
PixelGuide 6.	69.7	68.4	55.5	903 (38.3)	615 (26.1)	26,871	140,457	75.1	94.0	55.1	56.3	60.7	74.4	60.9	76.3	81.6	1.5	3,639 (0.0)	4,155 (0.0)	8.9
PixelGuide 6.	A. Boragule, H. Jang, N. Ha, M. Jeon. Pixel-Guided Association for Multi-Object Tracking. In Sensors, 2022.
NCT 7.	69.5	68.5	54.6	1,092 (46.4)	399 (16.9)	65,463	101,471	82.0	87.6	53.7	56.1	59.6	71.5	64.7	69.1	79.9	3.7	4,919 (0.0)	4,054 (0.0)	13.9
NCT 7.	K. Zeng, Y. You, T. Shen, Q. Wang, Z. Tao, Z. Wang, Q. Liu. NCT:noise-control multi-object tracking. In Complex & Intelligent Systems, 2023.
OUTrack_fm_p 8.	69.0	66.8	54.8	885 (37.6)	464 (19.7)	28,795	141,580	74.9	93.6	52.9	57.1	59.2	73.2	62.0	77.4	83.0	1.6	4,472 (0.0)	8,723 (0.0)	27.6
OUTrack_fm_p 8.	Q. Liu, D. Chen, Q. Chu, L. Yuan, B. Liu, L. Zhang, N. Yu. Online Multi-Object Tracking with Unsupervised Re-Identification Learning and Occlusion Estimation. In Neurocomputing, 2022.
TransCtr 9.	68.8	61.4	51.4	867 (36.8)	564 (23.9)	22,860	149,188	73.6	94.8	47.7	56.0	52.8	73.1	60.2	77.6	82.4	1.3	4,102 (0.0)	8,468 (0.0)	1.0
TransCtr 9.	Y. Xu, Y. Ban, G. Delorme, C. Gan, D. Rus, X. Alameda-Pineda. TransCenter: Transformers with Dense Queries for Multiple-Object Tracking. In arXiv, 2021.
TrajEocc 10.	67.8	61.4	50.0	848 (36.0)	577 (24.5)	20,950	157,460	72.1	95.1	46.9	53.9	55.4	67.3	57.9	76.4	81.4	1.2	3,475 (0.0)	5,663 (0.0)	1.4
TrajEocc 10.	A. Girbau, X. Gir'o-i-Nieto, I. Rius, F. Marqu'es. Multiple Object Tracking with Mixture Density Networks for Trajectory Estimation. In arXiv preprint arXiv:2106.10950, 2021.
Tracker	MOTA	IDF1	HOTA	MT	ML	FP	FN	Rcll	Prcn	AssA	DetA	AssRe	AssPr	DetRe	DetPr	LocA	FAF	ID Sw.	Frag	Hz
BYTE_Pub 11.	67.4	70.0	56.1	730 (31.0)	735 (31.2)	9,939	172,636	69.4	97.5	57.5	54.9	64.8	73.9	57.7	81.1	83.8	0.6	1,331 (0.0)	2,387 (0.0)	27.4
BYTE_Pub 11.	Y. Zhang, P. Sun, Y. Jiang, D. Yu, F. Weng, Z. Yuan, P. Luo, W. Liu, X. Wang. ByteTrack: Multi-Object Tracking by Associating Every Detection Box. In Proceedings of the European Conference on Computer Vision (ECCV), 2022.
TrajE 12.	67.4	61.2	49.7	820 (34.8)	587 (24.9)	18,652	161,347	71.4	95.6	46.6	53.5	54.4	67.8	57.3	76.7	81.5	1.1	4,019 (0.0)	6,613 (0.0)	1.4
TrajE 12.	A. Girbau, X. Gir'o-i-Nieto, I. Rius, F. Marqu'es. Multiple Object Tracking with Mixture Density Networks for Trajectory Estimation. In arXiv preprint arXiv:2106.10950, 2021.
MAT 13.	67.1	69.2	56.0	917 (38.9)	622 (26.4)	22,756	161,547	71.4	94.7	57.2	55.1	62.8	76.6	59.2	78.5	83.1	1.3	1,279 (17.9)	2,037 (28.5)	11.5
MAT 13.	MAT: Motion-Aware Multi-Object Tracking
HTracker 14.	66.9	70.4	55.3	667 (28.3)	490 (20.8)	30,704	151,001	73.2	93.1	55.5	55.3	60.9	74.8	59.9	76.2	81.6	1.7	4,806 (65.6)	10,994 (150.1)	36.7
HTracker 14.	Xudong Zhang. Boosting the tracking speed for real-time application.
hugmot 15.	64.8	62.8	49.4	738 (31.3)	645 (27.4)	16,174	180,371	68.0	96.0	48.4	50.8	54.1	71.0	54.0	76.2	80.7	0.9	2,102 (30.9)	7,750 (113.9)	92.5
hugmot 15.	Multiple Object Tracking by Tracjectory Map Regression with Temporal Priors Embedding
UTM 16.	63.5	65.1	52.5	881 (37.4)	635 (27.0)	33,683	170,352	69.8	92.1	53.2	52.2	59.7	73.4	57.0	75.2	81.9	1.9	1,686 (0.0)	2,562 (0.0)	13.1
UTM 16.	S. You, H. Yao, k. Bao, C. Xu. UTM: A Unified Multiple Object Tracking Model with Identity-Aware Feature Enhancement. In CVPR, 2023.
MPTC 17.	62.6	65.8	51.7	627 (26.6)	750 (31.8)	8,824	198,338	64.8	97.6	53.1	50.6	57.6	75.5	53.1	79.9	82.5	0.5	4,074 (0.0)	5,534 (0.0)	1.2
MPTC 17.	D. Stadler, J. Beyerer. Multi-Pedestrian Tracking with Clusters. In 2021 17th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), 2021.
TMOH 18.	62.1	62.8	50.4	633 (26.9)	739 (31.4)	10,951	201,195	64.3	97.1	50.9	50.2	54.8	77.2	52.7	79.5	82.4	0.6	1,897 (29.5)	4,622 (71.8)	0.7
TMOH 18.	D. Stadler, J. Beyerer. Improving Multiple Pedestrian Tracking by Track Management and Occlusion Handling. In CVPR, 2021.
SUSHI 19.	62.0	71.5	54.6	801 (34.0)	741 (31.5)	29,428	183,825	67.4	92.8	59.5	50.3	65.3	76.5	54.3	74.8	80.8	1.7	1,041 (0.0)	2,332 (0.0)	21.1
SUSHI 19.	O. Cetintas, G. Brasó, L. Leal-Taixé. Unifying Short and Long-Term Tracking with Graph Hierarchies. In The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2023.
UnsupTrack 20.	61.7	58.1	46.9	640 (27.2)	762 (32.4)	16,872	197,632	65.0	95.6	45.2	48.9	53.8	64.5	52.0	76.5	81.2	1.0	1,864 (28.7)	4,213 (64.8)	2.0
UnsupTrack 20.	S. Karthik, A. Prabhu, V. Gandhi. Simple Unsupervised Multi-Object Tracking. In Arxiv, 2020.
Tracker	MOTA	IDF1	HOTA	MT	ML	FP	FN	Rcll	Prcn	AssA	DetA	AssRe	AssPr	DetRe	DetPr	LocA	FAF	ID Sw.	Frag	Hz
Sp_Con 21.	61.5	63.3	50.5	622 (26.4)	754 (32.0)	14,056	200,655	64.4	96.3	52.0	49.2	55.3	79.3	52.0	77.8	81.7	0.8	2,478 (0.0)	5,079 (0.0)	7.7
Sp_Con 21.	G. Wang, Y. Wang, R. Gu, W. Hu, J. Hwang. Split and Connect: A Universal Tracklet Booster for Multi-Object Tracking. In IEEE Transactions on Multimedia, 2022.
CTTrackPub 22.	61.5	59.6	48.2	621 (26.4)	752 (31.9)	14,076	200,672	64.4	96.3	47.8	49.0	53.9	70.6	51.9	77.6	81.7	0.8	2,583 (40.1)	4,965 (77.1)	17.0
CTTrackPub 22.	X. Zhou, V. Koltun, P. Kr"ahenb"uhl. Tracking Objects as Points. In ECCV, 2020.
Lif_T 23.	60.5	65.6	51.3	637 (27.0)	791 (33.6)	14,966	206,619	63.4	96.0	54.7	48.3	59.0	78.6	51.1	77.4	81.3	0.8	1,189 (18.8)	3,476 (54.8)	0.5
Lif_T 23.	A. Hornakova, R. Henschel, B. Rosenhahn, P. Swoboda. Lifted Disjoint Paths with Application in Multiple Object Tracking. In ICML, 2020.
ApLift 24.	60.5	65.6	51.1	798 (33.9)	728 (30.9)	30,609	190,670	66.2	92.4	53.5	49.1	59.6	73.4	53.2	74.2	80.7	1.7	1,709 (0.0)	2,672 (0.0)	1.8
ApLift 24.	A. Hornakova, T. Kaiser, M. Rolinek, B. Rosenhahn, P. Swoboda, R. Henschel. Making Higher Order MOT Scalable: An Efficient Approximate Solver for Lifted Disjoint Paths. In International Conference on Computer Vision (ICCV), 2021.
mfi_tst 25.	60.1	58.8	47.2	612 (26.0)	699 (29.7)	13,503	209,475	62.9	96.3	46.4	48.3	49.7	76.7	50.9	78.0	81.5	0.8	2,065 (0.0)	3,829 (0.0)	2.2
mfi_tst 25.	J. Y, H. Ge, J. Yang, Y. Tong, S. Su. Online multi-object tracking using multi-function integration and tracking simulation training. In Applied Intelligence, 2021.
ISE_MOT17R 26.	60.1	56.4	45.8	672 (28.5)	661 (28.1)	23,168	199,483	64.6	94.0	43.2	49.0	47.7	72.0	52.3	76.1	81.0	1.3	2,556 (39.5)	3,182 (49.2)	7.2
ISE_MOT17R 26.	MIFT
SLA_public 27.	59.7	63.4	49.1	566 (24.0)	732 (31.1)	16,644	209,318	62.9	95.5	50.7	47.9	55.9	74.6	50.5	76.7	80.6	0.9	1,647 (26.2)	3,819 (60.7)	12.8
SLA_public 27.	Spatial-Attention Location-Aware Multi-Object Tracking. In , 2020.
LPC_MOT 28.	59.0	66.8	51.5	703 (29.9)	798 (33.9)	23,102	206,948	63.3	93.9	56.0	47.7	62.7	74.0	51.0	75.7	80.9	1.3	1,122 (17.7)	1,943 (30.7)	4.8
LPC_MOT 28.	P. Dai, R. Weng, W. Choi, C. Zhang, Z. He, W. Ding. Learning a Proposal Classifier for Multiple Object tracking. In CVPR (Accepted), 2021.
MPNTrack 29.	58.8	61.7	49.0	679 (28.8)	788 (33.5)	17,413	213,594	62.1	95.3	51.1	47.3	57.1	74.3	50.2	76.9	81.5	1.0	1,185 (19.1)	2,265 (36.4)	6.5
MPNTrack 29.	G. Braso, L. Leal-Taixe. Learning a Neural Solver for Multiple Object Tracking. In CVPR, 2020.
STMA 30.	58.8	61.6	48.4	617 (26.2)	763 (32.4)	17,564	213,561	62.1	95.2	49.8	47.3	56.0	72.6	50.1	76.8	81.2	1.0	1,446 (23.3)	3,430 (55.2)	1.2
STMA 30.	WHY, @BUAA
Tracker	MOTA	IDF1	HOTA	MT	ML	FP	FN	Rcll	Prcn	AssA	DetA	AssRe	AssPr	DetRe	DetPr	LocA	FAF	ID Sw.	Frag	Hz
IQHAT 31.	58.4	61.8	49.0	568 (24.1)	829 (35.2)	15,013	218,274	61.3	95.8	51.4	46.9	56.6	75.1	49.5	77.4	81.5	0.8	1,261 (0.0)	2,251 (0.0)	8.1
IQHAT 31.	Y. He, X. Wei, X. Hong, W. Ke, Y. Gong. Identity-Quantity Harmonic Multi-Object Tracking. In IEEE Transactions on Image Processing, 2022.
OCSORTpublic 32.	58.2	65.1	52.4	432 (18.3)	1,055 (44.8)	4,379	230,449	59.2	98.7	57.6	47.8	63.5	76.4	49.5	82.6	84.3	0.2	784 (0.0)	2,006 (0.0)	28.6
OCSORTpublic 32.	J. Cao, X. Weng, R. Khirodkar, J. Pang, K. Kitani. Observation-Centric SORT: Rethinking SORT for Robust Multi-Object Tracking. In , 2022.
Lif_TsimInt 33.	58.2	65.2	50.7	674 (28.6)	791 (33.6)	16,850	217,944	61.4	95.4	54.9	47.1	59.6	78.5	49.8	77.4	81.5	0.9	1,022 (16.7)	2,062 (33.6)	4.6
Lif_TsimInt 33.	A. Hornakova, R. Henschel, B. Rosenhahn, P. Swoboda. Lifted Disjoint Paths with Application in Multiple Object Tracking. In ICML, 2020.
GNNMatch 34.	57.3	56.3	45.4	575 (24.4)	787 (33.4)	14,100	225,042	60.1	96.0	45.2	45.9	52.5	67.9	48.4	77.4	81.5	0.8	1,911 (31.8)	2,837 (47.2)	1.3
GNNMatch 34.	I. Papakis, A. Sarkar, A. Karpatne. GCNNMatch: Graph Convolutional Neural Networks for Multi-Object Tracking via Sinkhorn Normalization. In arXiv preprint arXiv:2010.00067, 2020.
TG_CR 35.	57.1	59.3	47.5	554 (23.5)	823 (34.9)	15,209	224,841	60.2	95.7	49.4	46.0	53.9	77.2	48.5	77.2	81.3	0.9	1,766 (0.0)	2,349 (0.0)	8.9
TG_CR 35.	C. Ma, F. Yang, Y. Li, H. Jia, X. Xie, W. Gao. Deep trajectory post-processing and position projection for single & multiple camera multiple object tracking. In International Journal of Computer Vision, 2021.
UNS20regress 36.	56.8	58.3	46.4	538 (22.8)	880 (37.4)	11,567	230,645	59.1	96.6	47.9	45.3	51.0	79.0	47.6	77.8	81.4	0.7	1,320 (22.3)	2,061 (34.9)	72.8
UNS20regress 36.	F. Bastani, S. He, S. Madden. Self-Supervised Multi-Object Tracking with Cross-input Consistency. In Advances in Neural Information Processing Systems (NeurIPS), 2021.
GSM_Tracktor 37.	56.4	57.8	45.7	523 (22.2)	813 (34.5)	14,379	230,174	59.2	95.9	47.0	44.9	50.4	77.4	47.3	76.7	80.9	0.8	1,485 (25.1)	2,763 (46.7)	8.7
GSM_Tracktor 37.	Q. Liu, Q. Chu, B. Liu, N. Yu. Gsm: graph similarity model for multi-object tracking. In International Joint Conferences on Artificial Intelligence Organization, 2020.
Tracktor++v2 38.	56.3	55.1	44.8	498 (21.1)	831 (35.3)	8,866	235,449	58.3	97.4	45.1	44.9	48.3	78.4	47.0	78.6	81.8	0.5	1,987 (34.1)	3,763 (64.6)	1.5
Tracktor++v2 38.	P. Bergmann, T. Meinhardt, L. Leal-Taixé. Tracking without bells and whistles. In ICCV, 2019.
MOMOT_GLMB 39.	55.5	63.4	48.1	448 (19.0)	845 (35.9)	15,520	234,235	58.5	95.5	52.3	44.5	56.5	77.3	46.9	76.6	80.9	0.9	1,333 (22.8)	13,805 (236.0)	0.6
MOMOT_GLMB 39.	Mohammadjavad Abbaspour, Mohammad Ali Masnadi-Shirazi. Online Multi-Object Tracking with delta-GLMB Filter based on Occlusion and Identity Switch Handling. In arXiv preprint arXiv:2011.10111, 2020.
GMOT 40.	55.4	57.9	45.3	535 (22.7)	817 (34.7)	20,608	229,511	59.3	94.2	46.6	44.4	51.8	71.3	47.2	74.9	79.9	1.2	1,403 (23.7)	2,765 (46.6)	5.9
GMOT 40.	LXD, KHW @ HRI-SH
Tracker	MOTA	IDF1	HOTA	MT	ML	FP	FN	Rcll	Prcn	AssA	DetA	AssRe	AssPr	DetRe	DetPr	LocA	FAF	ID Sw.	Frag	Hz
TT17 41.	54.9	63.1	48.4	575 (24.4)	897 (38.1)	20,236	233,295	58.7	94.2	53.2	44.3	56.7	78.2	47.0	75.5	80.3	1.1	1,088 (18.6)	2,392 (40.8)	2.5
TT17 41.	Y. Zhang, H. Sheng, Y. Wu, S. Wang, W. Lyu, W. Ke, Z. Xiong. Long-term Tracking with Deep Tracklet Association. In IEEE Transactions on Image Processing, 2020.
LSST17 42.	54.7	62.3	47.1	480 (20.4)	944 (40.1)	26,091	228,434	59.5	92.8	51.0	43.8	56.0	72.5	46.9	73.1	79.3	1.5	1,243 (20.9)	3,726 (62.6)	1.5
LSST17 42.	Multi-Object Tracking with Multiple Cues and Switcher-Aware Classification
TADN 43.	54.6	49.0	39.7	528 (22.4)	711 (30.2)	36,285	214,857	61.9	90.6	35.1	45.3	43.1	59.3	49.4	72.2	80.1	2.0	4,869 (0.0)	7,821 (0.0)	360.9
TADN 43.	A. Psalta, V. Tsironis, K. Karantzalos. Transformer-based assignment decision network for multiple object tracking. In , 2022.
DGCT 44.	54.5	51.3	42.0	495 (21.0)	834 (35.4)	10,471	243,143	56.9	96.8	40.5	43.9	44.1	73.8	46.0	78.4	81.8	0.6	2,865 (50.3)	4,889 (85.9)	7.0
DGCT 44.	CJY, HYW, KHW @ HRI-SH
ISDH_HDAv2 45.	54.5	65.9	49.2	622 (26.4)	755 (32.1)	46,693	207,093	63.3	88.4	53.5	45.5	57.3	75.0	50.0	69.9	78.5	2.6	3,010 (47.6)	6,000 (94.8)	3.6
ISDH_HDAv2 45.	MM-008988/ IEEE Transactions on Multimedia
TPM 46.	54.2	52.6	41.5	536 (22.8)	882 (37.5)	13,739	242,730	57.0	95.9	40.9	42.5	44.8	71.7	44.8	75.5	80.0	0.8	1,824 (32.0)	2,472 (43.4)	0.8
TPM 46.	J. Peng, T. Wang, et.al. TPM: Multiple Object Tracking with Tracklet-Plane Matching. In Pattern Recognition, 2020.
HDTR 47.	54.1	48.4	46.8	549 (23.3)	819 (34.8)	18,002	238,818	57.7	94.8	48.9	44.9	54.3	74.1	47.6	78.2	82.6	1.0	1,895 (32.9)	2,693 (46.7)	1.8
HDTR 47.	M. Babaee, A. Athar, G. Rigoll. Multiple People Tracking Using Hierarchical Deep Tracklet Re-identification. In arXiv preprint arXiv:1811.04091, 2018.
TrctrD17 48.	53.7	53.8	42.4	458 (19.4)	861 (36.6)	11,731	247,447	56.1	96.4	42.7	42.5	46.8	73.4	44.6	76.6	80.4	0.7	1,947 (34.7)	4,792 (85.4)	4.9
TrctrD17 48.	Y. Xu, A. Osep, Y. Ban, R. Horaud, L. Leal-Taixe, X. Alameda-Pineda. How To Train Your Deep Multi-Object Tracker. In , 2019.
Tracktor++ 49.	53.5	52.3	42.1	459 (19.5)	861 (36.6)	12,201	248,047	56.0	96.3	41.7	42.9	45.4	75.6	45.0	77.3	80.9	0.7	2,072 (37.0)	4,611 (82.3)	1.5
Tracktor++ 49.	P. Bergmann, T. Meinhardt, L. Leal-Taixé. Tracking without bells and whistles. In ICCV, 2019.
SFS 50.	53.1	53.4	41.5	471 (20.0)	807 (34.3)	37,802	223,761	60.3	90.0	40.4	43.0	44.2	69.8	46.8	69.7	77.9	2.1	2,806 (46.5)	6,380 (105.7)	0.9
SFS 50.	MTSFS： Online Multi-Object Tracking Based on Salient Feature Selection in Crowded Scenes
Tracker	MOTA	IDF1	HOTA	MT	ML	FP	FN	Rcll	Prcn	AssA	DetA	AssRe	AssPr	DetRe	DetPr	LocA	FAF	ID Sw.	Frag	Hz
ENFT17 51.	52.8	57.1	44.5	543 (23.1)	867 (36.8)	26,754	237,909	57.8	92.4	46.2	43.2	50.6	74.0	46.3	74.0	80.4	1.5	1,667 (28.8)	2,557 (44.2)	0.5
ENFT17 51.	BUAA
LSST17O 52.	52.7	57.9	44.3	421 (17.9)	863 (36.6)	22,512	241,936	57.1	93.5	46.7	42.4	50.7	73.2	45.1	73.8	79.5	1.3	2,167 (37.9)	7,443 (130.3)	1.8
LSST17O 52.	Multi-Object Tracking with Multiple Cues and Switcher-Aware Classification
JBNOT 53.	52.6	50.8	41.3	465 (19.7)	844 (35.8)	31,572	232,659	58.8	91.3	39.8	43.3	43.0	73.9	46.9	72.8	80.2	1.8	3,050 (51.9)	3,792 (64.5)	5.4
JBNOT 53.	R. Henschel, Y. Zou, B. Rosenhahn. Multiple People Tracking using Body and Joint Detections. In CVPRW, 2019.
FAMNet 54.	52.0	48.7	0.0	450 (19.1)	787 (33.4)	14,138	253,616	55.1	95.6	0.0	0.0	0.0	0.0	0.0	0.0	100.0	0.8	3,072 (55.8)	5,318 (96.6)	0.0
FAMNet 54.	P. Chu, H. Ling. FAMNet: Joint Learning of Feature, Affinity and Multi-dimensional Assignment for Online Multiple Object Tracking. In ICCV, 2019.
eTC17 55.	51.9	58.1	44.9	544 (23.1)	836 (35.5)	36,164	232,783	58.7	90.2	47.0	43.3	51.7	72.1	46.9	72.0	79.4	2.0	2,288 (38.9)	3,071 (52.3)	0.7
eTC17 55.	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.
CMT 56.	51.8	60.7	46.9	462 (19.6)	1,009 (42.8)	29,528	240,960	57.3	91.6	51.6	42.8	55.5	77.1	46.0	73.6	80.3	1.7	1,217 (21.2)	2,008 (35.0)	6.5
CMT 56.	#Submission: TIP-21190-2019
eHAF17 57.	51.8	54.7	43.4	551 (23.4)	893 (37.9)	33,212	236,772	58.0	90.8	44.3	42.8	49.7	70.1	46.3	72.5	80.0	1.9	1,834 (31.6)	2,739 (47.2)	0.7
eHAF17 57.	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.
AFN17 58.	51.5	46.9	38.5	485 (20.6)	836 (35.5)	22,391	248,420	56.0	93.4	35.6	42.1	37.6	77.8	44.8	74.8	80.6	1.3	2,593 (46.3)	4,308 (77.0)	1.8
AFN17 58.	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.
BLSTM_MTP_O 59.	51.5	54.9	41.3	481 (20.4)	836 (35.5)	29,616	241,619	57.2	91.6	41.6	41.3	44.8	72.0	44.3	70.9	77.9	1.7	2,566 (0.0)	7,748 (0.0)	20.1
BLSTM_MTP_O 59.	C. Kim, F. Li, M. Alotaibi, J. Rehg. Discriminative Appearance Modeling with Multi-track Pooling for Real-time Multi-object Tracking. In CVPR (accepted), 2021.
CoCT_pub 60.	51.4	55.2	42.6	478 (20.3)	913 (38.8)	27,164	245,236	56.5	92.2	43.6	41.9	49.2	69.7	44.9	73.2	79.8	1.5	1,657 (0.0)	3,987 (0.0)	29.6
CoCT_pub 60.	https://ieeexplore.ieee.org/abstract/document/9247175
Tracker	MOTA	IDF1	HOTA	MT	ML	FP	FN	Rcll	Prcn	AssA	DetA	AssRe	AssPr	DetRe	DetPr	LocA	FAF	ID Sw.	Frag	Hz
YOONKJ17 61.	51.4	54.0	36.9	500 (21.2)	878 (37.3)	29,051	243,202	56.9	91.7	43.5	31.4	48.5	70.8	33.2	72.8	79.8	1.6	2,118 (37.2)	3,072 (54.0)	3.4
YOONKJ17 61.	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.
FWT 62.	51.3	47.6	39.0	505 (21.4)	830 (35.2)	24,101	247,921	56.1	92.9	36.8	41.9	39.8	72.3	44.7	74.0	80.0	1.4	2,648 (47.2)	4,279 (76.3)	0.2
FWT 62.	R. Henschel, L. Leal-Taixé, D. Cremers, B. Rosenhahn. Fusion of Head and Full-Body Detectors for Multi-Object Tracking. In Trajnet CVPRW, 2018.
NOTA 63.	51.3	54.5	42.3	403 (17.1)	833 (35.4)	20,148	252,531	55.2	93.9	43.6	41.3	47.6	72.8	43.8	74.5	79.9	1.1	2,285 (41.4)	5,798 (105.0)	17.8
NOTA 63.	L. Chen, H. Ai, R. Chen, Z. Zhuang. Aggregate Tracklet Appearance Features for Multi-Object Tracking. In IEEE Signal Processing Letters, 2019.
jCC 64.	51.2	54.5	42.5	493 (20.9)	872 (37.0)	25,937	247,822	56.1	92.4	44.2	41.2	46.7	77.9	44.0	72.6	79.2	1.5	1,802 (32.1)	2,984 (53.2)	1.8
jCC 64.	M. Keuper, S. Tang, B. Andres, T. Brox, B. Schiele. Motion Segmentation amp; Multiple Object Tracking by Correlation Co-Clustering. In IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018.
STRN_MOT17 65.	50.9	56.0	42.6	446 (18.9)	797 (33.8)	25,295	249,365	55.8	92.6	44.2	41.2	47.9	73.2	43.9	72.9	79.0	1.4	2,397 (43.0)	9,363 (167.8)	13.8
STRN_MOT17 65.	J. Xu, Y. Cao, Z. Zhang, H. Hu. Spatial-Temporal Relation Networks for Multi-Object Tracking. In ICCV, 2019.
MOTDT17 66.	50.9	52.7	41.2	413 (17.5)	841 (35.7)	24,069	250,768	55.6	92.9	41.4	41.3	45.6	70.3	44.1	73.7	79.7	1.4	2,474 (44.5)	5,317 (95.7)	18.3
MOTDT17 66.	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.
MHT_DAM 67.	50.7	47.2	38.9	491 (20.8)	869 (36.9)	22,875	252,889	55.2	93.2	36.7	41.6	38.3	80.2	44.3	74.8	80.6	1.3	2,314 (41.9)	2,865 (51.9)	0.9
MHT_DAM 67.	C. Kim, F. Li, A. Ciptadi, J. Rehg. Multiple Hypothesis Tracking Revisited. In ICCV, 2015.
TLMHT 68.	50.6	56.5	44.2	415 (17.6)	1,022 (43.4)	22,213	255,030	54.8	93.3	47.5	41.4	50.7	78.0	44.0	75.0	80.6	1.3	1,407 (25.7)	2,079 (37.9)	2.6
TLMHT 68.	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.
DEEP_TAMA 69.	50.3	53.5	42.0	453 (19.2)	883 (37.5)	25,479	252,996	55.2	92.4	43.3	41.0	46.9	73.1	43.8	73.4	79.7	1.4	2,192 (39.7)	3,978 (72.1)	1.5
DEEP_TAMA 69.	Y. Yoon, D. Kim, Y. Song, K. Yoon, M. Jeon. Online Multiple Pedestrians Tracking using Deep Temporal Appearance Matching Association. In Information Sciences, 2020.
EDMT17 70.	50.0	51.3	41.3	509 (21.6)	855 (36.3)	32,279	247,297	56.2	90.8	41.1	41.8	43.4	77.8	45.1	72.9	80.2	1.8	2,264 (40.3)	3,260 (58.0)	0.6
EDMT17 70.	J. Chen, H. Sheng, Y. Zhang, Z. Xiong. Enhancing Detection Model for Multiple Hypothesis Tracking. In BMTT-PETS CVPRw, 2017.
Tracker	MOTA	IDF1	HOTA	MT	ML	FP	FN	Rcll	Prcn	AssA	DetA	AssRe	AssPr	DetRe	DetPr	LocA	FAF	ID Sw.	Frag	Hz
GMPHDOGM17 71.	49.9	47.1	0.0	464 (19.7)	895 (38.0)	24,024	255,277	54.8	92.8	0.0	0.0	0.0	0.0	0.0	0.0	100.0	1.4	3,125 (57.1)	3,540 (64.7)	30.7
GMPHDOGM17 71.	Y. Song, K. Yoon, Y. Yoon, K. Yow, M. Jeon. Online Multi-Object Tracking with GMPHD Filter and Occlusion Group Management. In IEEE Access, 2019.
CS_MOT 72.	49.7	51.5	39.0	300 (12.7)	899 (38.2)	25,149	256,074	54.6	92.5	38.7	39.6	41.9	71.4	42.3	71.5	78.2	1.4	2,499 (45.8)	10,312 (188.8)	1.1
CS_MOT 72.	A Cost Matrix Optimization Method Based on Spatial Constraints under Hungarian Algorithm
MTDF17 73.	49.6	45.2	37.7	444 (18.9)	779 (33.1)	37,124	241,768	57.2	89.7	34.5	42.0	36.5	76.2	45.4	71.3	78.8	2.1	5,567 (97.4)	9,260 (162.0)	1.2
MTDF17 73.	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.
DASOT17 74.	49.5	51.8	41.5	481 (20.4)	814 (34.6)	33,640	247,370	56.2	90.4	41.7	41.6	44.4	75.6	44.9	72.3	79.9	1.9	4,142 (73.8)	6,852 (122.0)	9.1
DASOT17 74.	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.
PHD_GM 75.	48.8	43.2	0.0	449 (19.1)	830 (35.2)	26,260	257,971	54.3	92.1	0.0	0.0	0.0	0.0	0.0	0.0	100.0	1.5	4,407 (81.2)	6,448 (118.8)	22.3
PHD_GM 75.	R. Sanchez-Matilla, A. Cavallaro. Motion Prediction for First-person Vision Multi-object Tracking. In ECCVw, 2020.
OTCD_1 76.	48.6	47.9	38.4	382 (16.2)	970 (41.2)	18,499	268,204	52.5	94.1	37.6	39.6	40.4	73.8	41.8	74.9	80.0	1.0	3,502 (66.7)	5,588 (106.5)	15.5
OTCD_1 76.	Q. Liu, B. Liu, Y. Wu, W. Li, N. Yu. Real-Time Online Multi-Object Tracking in Compressed Domain. In IEEE Access, 2019.
HAM_SADF17 77.	48.3	51.1	40.3	402 (17.1)	981 (41.7)	20,967	269,038	52.3	93.4	41.5	39.3	44.4	75.8	41.7	74.5	80.2	1.2	1,871 (35.8)	3,020 (57.7)	5.0
HAM_SADF17 77.	Y. Yoon, A. Boragule, Y. Song, K. Yoon, M. Jeon. Online Multi-Object Tracking with Historical Appearance Matching and Scene Adaptive Detection Filtering. In IEEE AVSS, 2018.
DMAN 78.	48.2	55.7	42.5	454 (19.3)	902 (38.3)	26,218	263,608	53.3	92.0	46.1	39.4	49.9	72.9	42.0	72.5	79.0	1.5	2,194 (41.2)	5,378 (100.9)	0.3
DMAN 78.	J. Zhu, H. Yang, N. Liu, M. Kim, W. Zhang, M. Yang. Online Multi-Object Tracking with Dual Matching Attention Networks. In ECCV, 2018.
AM_ADM17 79.	48.1	52.1	40.5	316 (13.4)	934 (39.7)	25,061	265,495	52.9	92.3	42.0	39.5	45.1	73.8	42.1	73.4	79.8	1.4	2,214 (41.8)	5,027 (94.9)	5.7
AM_ADM17 79.	S. Lee, M. Kim, S. Bae, Learning Discriminative Appearance Models for Online Multi-Object Tracking with Appearance Discriminability Measures, In IEEE Access, 2018.
PHD_GSDL17 80.	48.0	49.6	39.2	402 (17.1)	838 (35.6)	23,199	265,954	52.9	92.8	39.2	39.5	43.4	70.2	42.1	73.9	80.1	1.3	3,998 (75.6)	8,886 (168.1)	6.7
PHD_GSDL17 80.	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.
Tracker	MOTA	IDF1	HOTA	MT	ML	FP	FN	Rcll	Prcn	AssA	DetA	AssRe	AssPr	DetRe	DetPr	LocA	FAF	ID Sw.	Frag	Hz
BBT 81.	47.8	50.0	40.0	449 (19.1)	936 (39.7)	35,552	255,118	54.8	89.7	40.3	40.0	45.3	66.8	43.3	71.0	79.2	2.0	3,676 (67.1)	4,949 (90.3)	121.6
BBT 81.	MTAP-D-20-01870 Tracking Subjects and Detecting Relationships in Crowded City Videos (under review)
MHT_bLSTM 82.	47.5	51.9	41.0	429 (18.2)	981 (41.7)	25,981	268,042	52.5	91.9	42.8	39.5	47.3	73.8	42.2	73.8	80.4	1.5	2,069 (39.4)	3,124 (59.5)	1.9
MHT_bLSTM 82.	C. Kim, F. Li, J. Rehg. Multi-object Tracking with Neural Gating Using Bilinear LSTM. In ECCV, 2018.
DAM_MOT 83.	47.0	48.7	0.0	397 (16.9)	897 (38.1)	28,933	267,896	52.5	91.1	0.0	0.0	0.0	0.0	0.0	0.0	100.0	1.6	2,140 (40.7)	2,756 (52.5)	18.7
DAM_MOT 83.	Multi Object Tracking using Deep Structural Cost Minimization in Data Association
MASS 84.	46.9	46.0	36.8	399 (16.9)	856 (36.3)	25,733	269,116	52.3	92.0	35.4	38.5	38.8	70.2	41.2	72.4	79.4	1.4	4,478 (85.6)	11,994 (229.3)	17.1
MASS 84.	H. Karunasekera, H. Wang, H. Zhang. Multiple Object Tracking With Attention to Appearance, Structure, Motion and Size. In IEEE Access, 2019.
GMPHD_Rd17 85.	46.8	54.1	41.5	464 (19.7)	784 (33.3)	38,452	257,678	54.3	88.9	43.5	39.9	47.2	75.0	43.3	70.9	79.5	2.2	3,865 (71.1)	8,097 (149.0)	30.8
GMPHD_Rd17 85.	N. Baisa. Occlusion-robust online multi-object visual tracking using a GM-PHD filter with CNN-based re-identification. In Journal of Visual Communication and Image Representation, 2021.
PHD_DCM 86.	46.5	47.6	38.2	397 (16.9)	875 (37.2)	23,859	272,430	51.7	92.4	37.9	38.8	41.1	73.0	41.3	73.8	80.2	1.3	5,649 (109.2)	9,298 (179.8)	1.6
PHD_DCM 86.	Z. Fu, F. Angelini, S. Naqvi, J. Chambers. GM-PHD Filter Based Online Multiple Human Tracking Using Deep Discriminative Correlation Matching. In 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2018.
PHD_LMP 87.	45.9	42.5	0.0	364 (15.5)	892 (37.9)	27,946	272,196	51.8	91.3	0.0	0.0	0.0	0.0	0.0	0.0	100.0	1.6	4,977 (96.2)	6,985 (135.0)	29.4
PHD_LMP 87.	R. Sanchez-Matilla, A. Cavallaro. Motion Prediction for First-person Vision Multi-object Tracking. In ECCVw, 2020.
EDA_GNN 88.	45.5	40.5	33.6	368 (15.6)	955 (40.6)	25,685	277,663	50.8	91.8	30.3	37.8	32.2	72.9	40.3	72.8	79.5	1.4	4,091 (80.5)	5,579 (109.8)	39.3
EDA_GNN 88.	Paper ID 2713
IOU17 89.	45.5	39.4	33.5	369 (15.7)	953 (40.5)	19,993	281,643	50.1	93.4	30.4	37.4	32.9	73.6	39.6	73.8	79.9	1.1	5,988 (119.6)	7,404 (147.8)	1,522.9
IOU17 89.	E. Bochinski, V. Eiselein, T. Sikora. High-Speed Tracking-by-Detection Without Using Image Information. In International Workshop on Traffic and Street Surveillance for Safety and Security at IEEE AVSS 2017, 2017.
HISP_DAL17 90.	45.4	39.9	34.0	349 (14.8)	922 (39.2)	21,820	277,473	50.8	92.9	30.7	38.1	32.2	77.3	40.5	74.1	80.2	1.2	8,727 (171.7)	7,147 (140.6)	3.2
HISP_DAL17 90.	N. Baisa. Robust online multi-target visual tracking using a HISP filter with discriminative deep appearance learning. In Journal of Visual Communication and Image Representation, 2021.
Tracker	MOTA	IDF1	HOTA	MT	ML	FP	FN	Rcll	Prcn	AssA	DetA	AssRe	AssPr	DetRe	DetPr	LocA	FAF	ID Sw.	Frag	Hz
LM_NN 91.	45.1	43.2	0.0	348 (14.8)	1,088 (46.2)	10,834	296,451	47.5	96.1	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.6	2,286 (48.2)	2,463 (51.9)	0.9
LM_NN 91.	M. Babaee, Z. Li, G. Rigoll. A Dual CNN--RNN for Multiple People Tracking. In Neurocomputing, 2019.
FPSN 92.	44.9	48.4	38.1	388 (16.5)	844 (35.8)	33,757	269,952	52.2	89.7	38.3	38.3	41.7	70.3	41.4	71.2	79.6	1.9	7,136 (136.8)	14,491 (277.8)	10.1
FPSN 92.	S. Lee, E. Kim. Multiple Object Tracking via Feature Pyramid Siamese Networks. In IEEE ACCESS, 2018.
HISP_T17 93.	44.6	38.8	33.3	355 (15.1)	913 (38.8)	25,478	276,395	51.0	91.9	29.5	38.1	31.1	76.3	40.7	73.2	80.1	1.4	10,617 (208.1)	7,487 (146.8)	4.7
HISP_T17 93.	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.
GMPHD_DAL 94.	44.4	36.2	31.4	350 (14.9)	927 (39.4)	19,170	283,380	49.8	93.6	26.6	37.5	29.1	69.7	39.7	74.6	80.4	1.1	11,137 (223.7)	13,900 (279.3)	3.4
GMPHD_DAL 94.	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.
SAS_MOT17 95.	44.2	57.2	43.0	379 (16.1)	1,044 (44.3)	29,473	283,611	49.7	90.5	50.1	37.0	53.7	76.3	39.7	72.2	79.5	1.7	1,529 (30.7)	2,644 (53.2)	4.8
SAS_MOT17 95.	A. Maksai, P. Fua. Eliminating Exposure Bias and Metric Mismatch in Multiple Object Tracking. In CVPR, 2019.
GMPHD_SHA 96.	43.7	39.2	33.2	276 (11.7)	1,012 (43.0)	25,935	287,758	49.0	91.4	30.6	36.6	32.3	77.3	39.0	72.7	79.6	1.5	3,838 (78.3)	5,056 (103.2)	9.2
GMPHD_SHA 96.	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.
SORT17 97.	43.1	39.8	34.0	295 (12.5)	997 (42.3)	28,398	287,582	49.0	90.7	31.8	37.0	33.1	79.6	39.5	73.1	80.5	1.6	4,852 (99.0)	7,127 (145.4)	143.3
SORT17 97.	A. Bewley, Z. Ge, L. Ott, F. Ramos, B. Upcroft. Simple online and realtime tracking. In 2016 IEEE International Conference on Image Processing (ICIP), 2016.
EAMTT 98.	42.6	41.8	0.0	300 (12.7)	1,006 (42.7)	30,711	288,474	48.9	90.0	0.0	0.0	0.0	0.0	0.0	0.0	100.0	1.7	4,488 (91.8)	5,720 (117.0)	12.0
EAMTT 98.	R. Sanchez-Matilla, F. Poiesi, A. Cavallaro. Online Multi-target Tracking with Strong and Weak Detections. In Computer Vision -- ECCV 2016 Workshops, 2016.
GMPHD_N1Tr 99.	42.1	33.9	30.3	280 (11.9)	1,005 (42.7)	18,214	297,646	47.2	93.6	25.9	36.1	27.3	76.2	38.0	75.2	80.5	1.0	10,698 (226.4)	10,864 (229.9)	9.9
GMPHD_N1Tr 99.	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.
FlowTracker 100.	40.4	38.0	32.5	330 (14.0)	863 (36.6)	60,962	269,136	52.3	82.9	28.9	37.1	30.4	76.8	41.6	66.0	78.8	3.4	5,927 (0.0)	6,748 (0.0)	61.2
FlowTracker 100.	H. Nishimura et al., "SDOF-Tracker: Fast and Accurate Multiple Human Tracking by Skipped-Detection and Optical-Flow"
Tracker	MOTA	IDF1	HOTA	MT	ML	FP	FN	Rcll	Prcn	AssA	DetA	AssRe	AssPr	DetRe	DetPr	LocA	FAF	ID Sw.	Frag	Hz
OVBT17 101.	40.2	43.6	34.6	151 (6.4)	1,169 (49.6)	27,497	305,179	45.9	90.4	34.9	34.7	36.9	75.3	36.9	72.7	80.1	1.5	4,763 (0.0)	8,711 (0.0)	1.7
OVBT17 101.	Y. Ban, S. Ba, X. Alameda-Pineda, R. Horaud. Tracking multiple persons based on a variational bayesian model. In European Conference on Computer Vision Workshops, 2016.
GMPHD_KCF 102.	39.6	36.6	30.3	208 (8.8)	1,019 (43.3)	50,903	284,228	49.6	84.6	26.4	35.3	28.0	72.8	38.9	66.3	77.8	2.9	5,811 (117.1)	7,414 (149.4)	3.3
GMPHD_KCF 102.	T. Kutschbach, E. Bochinski, V. Eiselein, T. Sikora. Sequential Sensor Fusion Combining Probability Hypothesis Density and Kernelized Correlation Filters for Multi-Object Tracking in Video Data. In International Workshop on Traffic and Street Surveillance for Safety and Security at IEEE AVSS 2017, 2017.
GM_PHD 103.	36.4	33.9	28.1	97 (4.1)	1,349 (57.3)	23,723	330,767	41.4	90.8	26.0	30.8	27.1	77.7	32.7	71.7	79.4	1.3	4,607 (111.3)	11,317 (273.5)	38.4
GM_PHD 103.	V. Eiselein, D. Arp, M. Pätzold, T. Sikora. Real-time Multi-Human Tracking using a Probability Hypothesis Density Filter and multiple detectors. In 9th IEEE International Conference on Advanced Video and Signal-Based Surveillance, 2012.
MOTR_test 104.	34.4	51.7	43.8	612 (26.0)	794 (33.7)	172,571	194,478	65.5	68.2	46.9	41.2	51.6	75.1	54.6	56.8	81.8	9.7	2,824 (0.0)	4,629 (0.0)	66.9
MOTR_test 104.	None

Sequences	Frames	Trajectories	Boxes
21	17757	2355	564228

Evaluation Measures

Lower is better. Higher is better.

Measure	Better	Perfect	Description
MOTA	higher	100%	Multi-Object Tracking Accuracy (+/- denotes standard deviation across all sequences) [1]. This measure combines three error sources: false positives, missed targets and identity switches.
IDF1	higher	100%	ID F1 Score [2]. The ratio of correctly identified detections over the average number of ground-truth and computed detections.
HOTA	higher	100%	Higher Order Tracking Accuracy [3]. Geometric mean of detection accuracy and association accuracy. Averaged across localization thresholds.
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).
Rcll	higher	100%	Ratio of correct detections to total number of GT boxes.
Prcn	higher	100%	Ratio of TP / (TP+FP).
AssA	higher	100%	Association Accuracy [3]. Association Jaccard index averaged over all matching detections and then averaged over localization thresholds.
DetA	higher	100%	Detection Accuracy [3]. Detection Jaccard index averaged over localization thresholds.
AssRe	higher	100%	Association Recall [3]. TPA / (TPA + FNA) averaged over all matching detections and then averaged over localization thresholds.
AssPr	higher	100%	Association Precision [3]. TPA / (TPA + FPA) averaged over all matching detections and then averaged over localization thresholds.
DetRe	higher	100%	Detection Recall [3]. TP /(TP + FN) averaged over localization thresholds.
DetPr	higher	100%	Detection Precision [3]. TP /(TP + FP) averaged over localization thresholds.
LocA	higher	100%	Localization Accuracy [3]. Average localization similarity averaged over all matching detections and averaged over localization thresholds.
FAF	lower	0	The average number of false alarms per frame.
ID Sw.	lower	0	Number of Identity Switches (ID switch ratio = #ID switches / recall) [4]. Please note that we follow the stricter definition of identity switches as described in the reference
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 a private 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]	Jonathon Luiten, A.O. & Leibe, B. HOTA: A Higher Order Metric for Evaluating Multi-Object Tracking. International Journal of Computer Vision, 2020.
[4]	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.

MOT17 Results

Download all results for this benchmark

Benchmark Statistics

Evaluation Measures

Legend

References: