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Re-ID Data in Real Surveillance Scenes

Home Page: https://www.nexdata.ai/datasets/1160?source=Github

computer-vision dataset deep-learning object-recognition object-tracking re-id re-identification surveillance-video

11130-people-re-id-data-in-real-surveillance-scenes's Introduction

11130-People-Re-ID-Data-in-Real-Surveillance-Scenes

Description

11,130 People - Re-ID Data in Real Surveillance Scenes. The data includes indoor scenes and outdoor scenes. The data includes males and females, and the age distribution is from children to the elderly. The data diversity includes different age groups, different time periods, different shooting angles, different human body orientations and postures, clothing for different seasons. For annotation, the rectangular bounding boxes and 15 attributes of human body were annotated. This data can be used for re-id and other tasks.

For more details, please refer to the link: https://www.nexdata.ai/datasets/1160?source=Github

Data size

11,130 people, about 10-27 images per person

Population distribution

the race distribution is Asian, the gender distribution is male and female, the age distribution is from children to the elderly

Collecting environment

including indoor and outdoor scenes (such as supermarket, mall and residential area, etc.)

Data diversity

different ages, different time periods, different cameras, different human body orientations and postures, different ages collecting environment

Device

surveillance cameras, the image resolution is not less than 1,920*1,080

Data format

the image data format is .jpg, the annotation file format is .json

Annotation content

human body rectangular bounding boxes, 15 human body attributes

Quality Requirements

A rectangular bounding box of human body is qualified when the deviation is not more than 3 pixels, and the qualified rate of the bounding boxes shall not be lower than 97%;Annotation accuracy of attributes is over 97%

Licensing Information

Commercial License

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