DukeMTMCT Results

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

Easy Test Set

Single Camera (all)

Tracker	IDF1	IDP	IDR	MOTA	MOTP	FAF	MT	ML	FP	FN	ID Sw.	Frag
MTMC_basel 1.	91.3	91.8	90.9	90.7	78.7	0.06	1,169	14	44,492	54,044	153	456
MTMC_basel 1.	Anonymous submission
MTMCT_PA 2.	87.5	87.5	87.5	89.1	78.8	0.08	1,165	14	57,343	57,848	229	376
MTMCT_PA 2.	Anonymous submission
MIS_ncku 3.	88.8	89.5	88.2	90.0	78.8	0.06	1,148	17	45,155	60,487	278	404
MIS_ncku 3.	Anonymous submission
MTMCT_s 4.	91.3	92.8	89.8	90.6	78.3	0.04	1,165	11	32,314	66,824	178	467
MTMCT_s 4.	Anonymous submission
MTMCT_sp 5.	91.4	92.9	90.0	90.4	78.2	0.05	1,164	12	34,054	67,079	214	457
MTMCT_sp 5.	Anonymous submission
MTMC_TBC 6.	89.2	90.4	88.0	87.8	78.1	0.07	1,131	22	50,771	78,263	222	833
MTMC_TBC 6.	Anonymous submission
d_b_v13 7.	89.0	91.1	87.0	88.4	78.6	0.05	1,112	8	37,509	84,838	724	1,954
d_b_v13 7.	Anonymous submission
MTMC_ReID 8.	89.8	92.0	87.7	88.2	79.0	0.05	1,123	17	37,911	86,958	372	772
MTMC_ReID 8.	Z. Zhang, J. Wu, X. Zhang, C. Zhang. Multi-Target, Multi-Camera Tracking by Hierarchical Clustering: Recent Progress on DukeMTMC Project. In , 2017.
DeepCC 9.	89.2	91.7	86.7	87.5	77.1	0.05	1,103	29	37,280	94,399	202	753
DeepCC 9.	E. Ristani, C. Tomasi. Features for Multi-Target Multi-Camera Tracking and Re-Identification. In CVPR, 2018.
MFFusion 10.	86.7	88.4	85.0	84.6	78.2	0.08	1,092	24	61,155	101,871	299	775
MFFusion 10.	Anonymous submission
Tracker	IDF1	IDP	IDR	MOTA	MOTP	FAF	MT	ML	FP	FN	ID Sw.	Frag
TAREIDMTMC 11.	83.8	87.6	80.4	83.3	75.5	0.06	1,051	17	44,691	131,220	383	2,428
TAREIDMTMC 11.	N. Jiang, S. Bai, Y. Xu, C. Xing, Z. Zhou, W. Wu. Online Inter-Camera Trajectory Association Exploiting Person Re-Identification and Camera Topology. In MM, 2018.
mtmc_cg 12.	49.4	52.0	47.1	83.7	78.8	0.05	997	22	35,397	134,982	2,426	687
mtmc_cg 12.	Anonymous submission
MOT_TBA 13.	82.4	86.1	79.0	79.6	80.4	0.09	1,026	46	64,002	151,483	875	1,481
MOT_TBA 13.	Paper ID 648
SAS_full 14.	84.0	89.4	79.2	76.0	76.0	0.09	950	72	66,783	186,974	169	1,256
SAS_full 14.	Anonymous submission
MYTRACKER 15.	80.3	87.3	74.4	78.3	78.4	0.05	914	72	35,580	193,253	406	1,116
MYTRACKER 15.	K. Yoon, Y. Song, M. Jeon. Multiple hypothesis tracking algorithm for multi-target multi-camera tracking with disjoint views. In IET Image Processing, 2018.
SAS 16.	76.5	83.9	70.3	69.3	74.8	0.10	813	89	76,059	248,224	426	2,081
SAS 16.	Submission id 177
MTMC_CDSC 17.	77.0	87.6	68.6	70.9	75.8	0.05	740	110	38,655	268,398	693	4,717
MTMC_CDSC 17.	Y. Tesfaye, E. Zemene, A. Prati, M. Pelillo, M. Shah. Multi-Target Tracking in Multiple Non-Overlapping Cameras using Constrained Dominant Sets. In CoRR, 2017.
MTMC_ReIDp 18.	79.2	89.9	70.7	68.8	77.9	0.07	726	143	52,408	277,762	449	1,060
MTMC_ReIDp 18.	Z. Zhang, J. Wu, X. Zhang, C. Zhang. Multi-Target, Multi-Camera Tracking by Hierarchical Clustering: Recent Progress on DukeMTMC Project. In , 2017.
mtmct_hxq 19.	16.4	19.4	14.2	68.4	74.1	0.03	817	264	25,155	308,662	918	4,392
mtmct_hxq 19.	Anonymous submission
MTMC___DS 20.	70.8	84.3	61.0	65.2	75.8	0.05	603	119	36,956	330,382	855	4,669
MTMC___DS 20.	Anonymous submission
Tracker	IDF1	IDP	IDR	MOTA	MOTP	FAF	MT	ML	FP	FN	ID Sw.	Frag
PT_BIPCC 21.	71.2	84.8	61.4	59.3	78.7	0.09	666	234	68,634	361,589	290	783
PT_BIPCC 21.	A. Maksai, X. Wang, F. Fleuret, P. Fua. Non-Markovian Globally Consistent Multi-Object Tracking. In ICCV, 2017.
BIPCC 22.	70.1	83.6	60.4	59.4	78.7	0.09	665	234	68,147	361,672	300	801
BIPCC 22.	E. Ristani, F. Solera, R. Zou, R. Cucchiara, C. Tomasi. Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking. In ECCV workshop on Benchmarking Multi-Target Tracking, 2016.
dirBIPCC 23.	70.0	83.2	60.4	59.0	78.7	0.10	665	234	71,381	361,673	298	799
dirBIPCC 23.	Anonymous submission
lx_b 24.	70.3	88.1	58.5	61.3	78.7	0.04	640	247	26,845	382,524	246	788
lx_b 24.	Anonymous submission

Multi-Camera

Tracker	IDF1	IDP	IDR
mtmct_hxq 1.	5.9	7.0	5.1
	Anonymous submission
MFFusion 2.	26.5	27.0	26.0
	Anonymous submission
MTMC_TBC 3.	27.7	28.1	27.3
	Anonymous submission
MOT_TBA 4.	28.5	29.8	27.4
	Paper ID 648
PT_BIPCC 5.	34.9	41.6	30.1
	A. Maksai, X. Wang, F. Fleuret, P. Fua. Non-Markovian Globally Consistent Multi-Object Tracking. In ICCV, 2017.
SAS 6.	38.5	42.3	35.4
	Submission id 177
mtmc_cg 7.	40.7	42.8	38.8
	Anonymous submission
dirBIPCC 8.	52.1	62.0	45.0
	Anonymous submission
MTMC___DS 9.	54.9	65.4	47.3
	Anonymous submission
BIPCC 10.	56.2	67.0	48.4
	E. Ristani, F. Solera, R. Zou, R. Cucchiara, C. Tomasi. Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking. In ECCV workshop on Benchmarking Multi-Target Tracking, 2016.
Tracker	IDF1	IDP	IDR
lx_b 11.	58.0	72.6	48.2
	Anonymous submission
SAS_full 12.	59.9	63.8	56.5
	Anonymous submission
MTMC_CDSC 13.	60.0	68.3	53.5
	Y. Tesfaye, E. Zemene, A. Prati, M. Pelillo, M. Shah. Multi-Target Tracking in Multiple Non-Overlapping Cameras using Constrained Dominant Sets. In CoRR, 2017.
MYTRACKER 14.	65.4	71.1	60.6
	K. Yoon, Y. Song, M. Jeon. Multiple hypothesis tracking algorithm for multi-target multi-camera tracking with disjoint views. In IET Image Processing, 2018.
TAREIDMTMC 15.	68.8	71.8	66.0
	N. Jiang, S. Bai, Y. Xu, C. Xing, Z. Zhou, W. Wu. Online Inter-Camera Trajectory Association Exploiting Person Re-Identification and Camera Topology. In MM, 2018.
MTMCT_PA 16.	72.4	72.5	72.4
	Anonymous submission
MTMC_ReIDp 17.	74.4	84.4	66.4
	Z. Zhang, J. Wu, X. Zhang, C. Zhang. Multi-Target, Multi-Camera Tracking by Hierarchical Clustering: Recent Progress on DukeMTMC Project. In , 2017.
DeepCC 18.	82.0	84.4	79.8
	E. Ristani, C. Tomasi. Features for Multi-Target Multi-Camera Tracking and Re-Identification. In CVPR, 2018.
MTMC_ReID 19.	83.2	85.2	81.2
	Z. Zhang, J. Wu, X. Zhang, C. Zhang. Multi-Target, Multi-Camera Tracking by Hierarchical Clustering: Recent Progress on DukeMTMC Project. In , 2017.
MIS_ncku 20.	84.6	85.3	84.0
	Anonymous submission
Tracker	IDF1	IDP	IDR
d_b_v13 21.	85.4	87.4	83.5
	Anonymous submission
MTMCT_s 22.	87.4	88.8	85.9
	Anonymous submission
MTMC_basel 23.	87.4	87.8	87.0
	Anonymous submission
MTMCT_sp 24.	88.0	89.4	86.6
	Anonymous submission

Hard Test Set

Single Camera (all)

Tracker	IDF1	IDP	IDR	MOTA	MOTP	FAF	MT	ML	FP	FN	ID Sw.	Frag
MTMC_basel 1.	83.7	88.8	79.1	78.9	76.6	0.13	614	34	35,987	114,104	307	602
MTMC_basel 1.	Anonymous submission
MTMCT_PA 2.	80.3	84.9	76.2	77.5	76.6	0.15	600	32	43,666	116,079	469	572
MTMCT_PA 2.	Anonymous submission
MIS_ncku 3.	80.3	85.3	75.9	78.0	76.6	0.14	599	31	38,918	117,627	468	627
MIS_ncku 3.	Anonymous submission
MTMCT_s 4.	83.5	89.6	78.2	79.1	76.3	0.10	607	31	29,068	119,967	321	512
MTMCT_s 4.	Anonymous submission
MTMCT_sp 5.	83.2	89.3	78.0	78.8	76.2	0.11	606	32	30,259	120,511	375	580
MTMCT_sp 5.	Anonymous submission
MTMC_TBC 6.	82.4	90.0	76.0	75.2	76.4	0.12	569	52	32,813	143,883	298	898
MTMC_TBC 6.	Anonymous submission
d_b_v13 7.	82.3	90.7	75.4	76.2	76.7	0.09	536	38	24,319	144,813	910	2,024
d_b_v13 7.	Anonymous submission
MFFusion 8.	79.3	86.6	73.2	74.6	76.3	0.12	552	52	35,193	145,514	467	970
MFFusion 8.	Anonymous submission
MTMC_ReID 9.	81.2	89.4	74.5	74.7	76.6	0.11	569	57	30,297	149,443	521	860
MTMC_ReID 9.	Z. Zhang, J. Wu, X. Zhang, C. Zhang. Multi-Target, Multi-Camera Tracking by Hierarchical Clustering: Recent Progress on DukeMTMC Project. In , 2017.
DeepCC 10.	79.0	87.4	72.0	70.0	75.0	0.15	524	66	43,989	170,104	236	777
DeepCC 10.	E. Ristani, C. Tomasi. Features for Multi-Target Multi-Camera Tracking and Re-Identification. In CVPR, 2018.
Tracker	IDF1	IDP	IDR	MOTA	MOTP	FAF	MT	ML	FP	FN	ID Sw.	Frag
mtmc_cg 11.	42.1	47.6	37.7	70.7	76.9	0.10	479	44	29,660	177,712	1,774	821
mtmc_cg 11.	Anonymous submission
TAREIDMTMC 12.	77.9	86.6	70.7	68.1	73.5	0.17	498	45	47,777	179,035	541	2,074
TAREIDMTMC 12.	N. Jiang, S. Bai, Y. Xu, C. Xing, Z. Zhou, W. Wu. Online Inter-Camera Trajectory Association Exploiting Person Re-Identification and Camera Topology. In MM, 2018.
SAS_full 13.	76.8	89.3	67.4	65.4	75.3	0.12	450	87	35,596	210,639	267	977
SAS_full 13.	Anonymous submission
mtmct_hxq 14.	49.0	58.2	42.4	66.0	72.7	0.08	463	78	22,099	216,618	3,620	5,193
mtmct_hxq 14.	Anonymous submission
MYTRACKER 15.	63.5	73.9	55.6	59.6	76.7	0.19	400	80	55,038	231,527	1,468	1,801
MYTRACKER 15.	K. Yoon, Y. Song, M. Jeon. Multiple hypothesis tracking algorithm for multi-target multi-camera tracking with disjoint views. In IET Image Processing, 2018.
MTMC_ReIDp 16.	71.6	85.3	61.7	60.9	76.8	0.14	375	104	40,732	237,974	572	993
MTMC_ReIDp 16.	Z. Zhang, J. Wu, X. Zhang, C. Zhang. Multi-Target, Multi-Camera Tracking by Hierarchical Clustering: Recent Progress on DukeMTMC Project. In , 2017.
SAS 17.	65.5	79.3	55.8	59.1	74.0	0.14	379	102	39,576	251,256	972	1,855
SAS 17.	Submission id 177
MTMC_CDSC 18.	65.5	81.4	54.7	59.6	75.4	0.09	348	99	26,643	260,073	1,637	5,024
MTMC_CDSC 18.	Y. Tesfaye, E. Zemene, A. Prati, M. Pelillo, M. Shah. Multi-Target Tracking in Multiple Non-Overlapping Cameras using Constrained Dominant Sets. In CoRR, 2017.
MTMC___DS 19.	62.6	79.4	51.6	57.3	75.3	0.09	314	103	26,721	276,233	1,672	4,946
MTMC___DS 19.	Anonymous submission
dirBIPCC 20.	64.3	80.3	53.6	53.9	77.1	0.16	338	102	45,472	283,014	653	1,078
dirBIPCC 20.	Anonymous submission
Tracker	IDF1	IDP	IDR	MOTA	MOTP	FAF	MT	ML	FP	FN	ID Sw.	Frag
BIPCC 21.	64.5	81.2	53.5	54.6	77.1	0.14	338	103	39,599	283,376	652	1,073
BIPCC 21.	E. Ristani, F. Solera, R. Zou, R. Cucchiara, C. Tomasi. Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking. In ECCV workshop on Benchmarking Multi-Target Tracking, 2016.
PT_BIPCC 22.	65.0	81.8	54.0	54.4	77.1	0.14	335	104	40,978	283,704	661	1,054
PT_BIPCC 22.	A. Maksai, X. Wang, F. Fleuret, P. Fua. Non-Markovian Globally Consistent Multi-Object Tracking. In ICCV, 2017.
lx_b 23.	64.2	80.4	53.4	53.6	77.1	0.16	336	107	45,370	285,192	621	1,049
lx_b 23.	Anonymous submission

Multi-Camera

Tracker	IDF1	IDP	IDR
mtmct_hxq 1.	20.1	23.9	17.4
	Anonymous submission
MFFusion 2.	30.2	32.9	27.8
	Anonymous submission
MTMC_TBC 3.	31.3	34.2	28.9
	Anonymous submission
mtmc_cg 4.	32.4	36.6	29.0
	Anonymous submission
PT_BIPCC 5.	32.9	41.3	27.3
	A. Maksai, X. Wang, F. Fleuret, P. Fua. Non-Markovian Globally Consistent Multi-Object Tracking. In ICCV, 2017.
SAS 6.	33.0	40.0	28.1
	Submission id 177
dirBIPCC 7.	45.0	56.3	37.5
	Anonymous submission
BIPCC 8.	47.3	59.6	39.2
	E. Ristani, F. Solera, R. Zou, R. Cucchiara, C. Tomasi. Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking. In ECCV workshop on Benchmarking Multi-Target Tracking, 2016.
MTMC___DS 9.	47.6	60.4	39.3
	Anonymous submission
lx_b 10.	48.3	60.6	40.2
	Anonymous submission
Tracker	IDF1	IDP	IDR
MYTRACKER 11.	50.1	58.3	43.9
	K. Yoon, Y. Song, M. Jeon. Multiple hypothesis tracking algorithm for multi-target multi-camera tracking with disjoint views. In IET Image Processing, 2018.
MTMC_CDSC 12.	50.9	63.2	42.6
	Y. Tesfaye, E. Zemene, A. Prati, M. Pelillo, M. Shah. Multi-Target Tracking in Multiple Non-Overlapping Cameras using Constrained Dominant Sets. In CoRR, 2017.
SAS_full 13.	51.7	60.1	45.4
	Anonymous submission
TAREIDMTMC 14.	61.2	68.0	55.5
	N. Jiang, S. Bai, Y. Xu, C. Xing, Z. Zhou, W. Wu. Online Inter-Camera Trajectory Association Exploiting Person Re-Identification and Camera Topology. In MM, 2018.
MTMCT_PA 15.	65.5	69.2	62.2
	Anonymous submission
MTMC_ReIDp 16.	65.6	78.1	56.5
	Z. Zhang, J. Wu, X. Zhang, C. Zhang. Multi-Target, Multi-Camera Tracking by Hierarchical Clustering: Recent Progress on DukeMTMC Project. In , 2017.
DeepCC 17.	68.5	75.9	62.4
	E. Ristani, C. Tomasi. Features for Multi-Target Multi-Camera Tracking and Re-Identification. In CVPR, 2018.
MIS_ncku 18.	71.7	76.2	67.8
	Anonymous submission
MTMC_ReID 19.	74.0	81.4	67.8
	Z. Zhang, J. Wu, X. Zhang, C. Zhang. Multi-Target, Multi-Camera Tracking by Hierarchical Clustering: Recent Progress on DukeMTMC Project. In , 2017.
MTMC_basel 20.	75.4	80.0	71.3
	Anonymous submission
Tracker	IDF1	IDP	IDR
MTMCT_sp 21.	76.2	81.7	71.4
	Anonymous submission
MTMCT_s 22.	77.2	82.8	72.2
	Anonymous submission
d_b_v13 23.	78.5	86.4	71.8
	Anonymous submission

Evaluation Measures

Lower is better. Higher is better.

Measure	Better	Perfect	Description
IDF1	higher	100 %	ID F1 Score [1]. The ratio of correctly identified detections over the average number of ground-truth and computed detections.
IDP	higher	100 %	ID Precision [1]. Identification precision is the fraction of computed detections that are correctly identified.
IDR	higher	100 %	ID Recall [1]. Identification recall is the fraction of ground truth detections that are correctly identified.
MOTA	higher	100 %	Multiple Object Tracking Accuracy [2]. This measure combines three error sources: false positives, missed targets and identity switches.
MOTP	higher	100 %	Multiple Object Tracking Precision [2]. The misalignment between the annotated and the predicted bounding boxes.
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).

Legend

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

References:

[1]	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.
[2]	Bernardin, K. & Stiefelhagen, R. Evaluating Multiple Object Tracking Performance: The CLEAR MOT Metrics. Image and Video Processing, 2008(1):1-10, 2008.
[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.