MOT Challenge

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Rendering of new sequences is currently deactivated due to heavy load.

Rendering of new sequences is currently deactivated due to heavy load.

Rendering of new sequences is currently deactivated due to heavy load.

Rendering of new sequences is currently deactivated due to heavy load.

Rendering of new sequences is currently deactivated due to heavy load.

Rendering of new sequences is currently deactivated due to heavy load.

Rendering of new sequences is currently deactivated due to heavy load.

Rendering of new sequences is currently deactivated due to heavy load.

Rendering of new sequences is currently deactivated due to heavy load.

Benchmark:

MOT15 |

Short name:

QuadMOT

Detector:

Public

Description:

n/a

Reference:

J. Son, M. Baek, M. Cho, B. Han. Multi-Object Tracking with Quadruplet Convolutional Neural Networks. In CVPR, 2017.

Last submitted:

November 14, 2016 (8 years ago)

Published:

November 23, 2016 at 04:26:12 CET

Submissions:

Project page / code:

n/a

Open source:

Hardware:

3 GHZ, 1 Core, TITAN X

Runtime:

3.7 Hz

Benchmark performance:

Sequence	MOTA	IDF1	HOTA	MT	ML	FP	FN	Rcll	Prcn	AssA	DetA	AssRe	AssPr	DetRe	DetPr	LocA	FAF	ID Sw.	Frag
MOT15	33.8	40.4	0.0	93 (12.9)	266 (36.9)	7,898	32,061	47.8	78.8	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.4	703 (14.7)	1,430 (29.9)

Detailed performance:

Sequence	MOTA	IDF1	HOTA	MT	ML	FP	FN	Rcll	Prcn	AssA	DetA	AssRe	AssPr	DetRe	DetPr	LocA	FAF	ID Sw.	Frag
ADL-Rundle-1	13.6	35.6	0.0	5	8	3,207	4,773	48.7	58.6	0.0	0.0	0.0	0.0	0.0	0.0	0.0	6.4	56	103
ADL-Rundle-3	38.4	39.2	0.0	3	11	302	5,911	41.9	93.4	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.5	52	60
AVG-TownCentre	30.8	49.4	0.0	41	71	1,191	3,643	49.0	74.6	0.0	0.0	0.0	0.0	0.0	0.0	0.0	2.6	111	409
ETH-Crossing	33.4	53.0	0.0	2	15	41	627	37.5	90.2	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.2	0	5
ETH-Jelmoli	42.3	55.4	0.0	8	13	379	1,065	58.0	79.5	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.9	19	50
ETH-Linthescher	28.7	40.3	0.0	14	124	270	6,056	32.2	91.4	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.2	38	81
KITTI-16	38.3	49.9	0.0	0	3	219	801	52.9	80.4	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	29	60
KITTI-19	33.8	47.9	0.0	5	13	1,059	2,417	54.8	73.4	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	60	191
PETS09-S2L2	49.0	28.8	0.0	7	3	686	3,947	59.1	89.2	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.6	285	380
TUD-Crossing	72.1	59.8	0.0	6	0	5	288	73.9	99.4	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	15	21
Venice-1	31.8	35.7	0.0	2	5	539	2,533	44.5	79.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.2	38	70

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QuadMOT: Multi-Object Tracking with Quadruplet Convolutional Neural Networks