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https://www.nexdata.ai/shujutang/static/image/index/datatang_tuxiang_default.webp
[]
2,769 People CCTV Person Re-Identification Dataset in Europe
CCTV re-id dataset
person re-identification data
pedestrian dataset
surveillance dataset
human re-id Europe
multi-camera reid dataset
pedestrian attributes data
computer vision reid
The 2,769 People CCTV Re-Identification Dataset in Europe provides high-quality pedestrian data for computer vision research. The data includes both males and females, with a racial distribution of Caucasian, Black, and Asian, and an age range from children to the elderly. The data diversity includes different age groups, different time periods, different cameras, and different body orientations and poses. Each person is annotated with rectangular bounding boxes and 15 body attributes. This data can be use for person re-identification, pedestrian tracking, surveillance analysis, and multi-camera AI models.
This is a paid dataset licensed for commercial use. Ready-made datasets are available for immediate integration into AI projects.
![Specifications]()
Specifications
Data size
2,769 people, 1-25 cameras for each person
Population distribution
race distribution: 2,646 Caucasians, 47 Asians, 76 blacks; gender distribution: 1,091 males, 1,678 females; age distribution: mainly young and middle-aged
Collecting environment
department store
Data diversity
different age groups, different time periods, different cameras, different human body orientations and postures
Device
surveillance cameras, the resolution includes 960*576 and 1,440*1,616
Collecting angle
looking down angle
Collecting time
10:00-20:00
Data format
the image data format is .jpg or png, the annotation file format is .json
Annotation content
human body rectangular bounding boxes, 15 human body attributes
Accuracy rate
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 human attributes is over 97%; the accuracy of label annotation is not less than 97%
![Sample]()
Sample
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28de2d1e-be2b-4762-a5e4-eb7107812c66
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