MOT Challenge

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Benchmark:

CVPR 2019 Detection Challenge |

Short name:

ViPeD_19

Description:

n/a

Reference:

G. Amato, L. Ciampi, F. Falchi, C. Gennaro, N. Messina. Learning pedestrian detection from virtual worlds. In International Conference on Image Analysis and Processing, 2019.

Last submitted:

June 13, 2019 (5 years ago)

Published:

July 05, 2019 at 23:52:29 CET

Submissions:

Project page / code:

n/a

Open source:

Yes

Hardware:

RTX 2080Ti

Runtime:

11.2 Hz

Benchmark performance:

Sequence	AP	MODA	MODP	FAF	TP	FP	FN	Rcll	Prcn	F1
CVPR 2019 Detection Challenge	0.73	39.0	71.5	35.7	308,545	159,983	71,997	81.1	65.9	72.7

Detailed performance:

Sequence	AP	MODA	MODP	FAF	TP	FP	FN	Rcll	Prcn	F1
CVPR19-04	0.77	57.4	71.0	32.3	221,665	67,191	47,282	82.4	76.7	79.5
CVPR19-06	0.65	-7.8	72.4	51.0	46,539	51,409	16,186	74.2	47.5	57.9
CVPR19-07	0.89	65.9	74.9	7.0	14,858	4,109	1,460	91.1	78.3	84.2
CVPR19-08	0.61	-36.2	71.7	46.2	25,483	37,274	7,069	78.3	40.6	53.5

Raw data:

download

ViPeD_19

ViPeD_19

MOT19_2

ViPeD_19: YOLO detector trained using JTA