en

Please fill in your name

Mobile phone format error

Please enter the telephone

Please enter your company name

Please enter your company email

Please enter the data requirement

Successful submission! Thank you for your support.

Format error, Please fill in again

Confirm

The data requirement cannot be less than 5 words and cannot be pure numbers

11,130 People - Re-ID Data in Real Surveillance Scenes

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

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.

Paid Datasets
This is a paid datasets for commercial use, research purpose and more. Licensed ready made datasets help jump-start AI projects.
SpecificationsSpecifications
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%
Sample Sample
  • Waiting For Data
Recommended DatasetsRecommended Dataset
5,521 People - Re-ID Data in Surveillance Scenes

5,521 People - Re-ID Data in 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. The data can be used for re-id and other tasks.

Surveillance scenes Re-ID different age groups different time periods different shooting angles different human body orientations and postures clothing for different seasons human body rectangular bounding boxes attributes annotation
10,034 People - Re-ID Data in Surveillance Scenes

10,034 People - Re-ID Data in Surveillance Scenes. The data includes supermarket (inside supermarket and at the gate of the supermarket) scenes. The data includes males and females and the age distribution is from children to the elderly. In this dataset, the rectangular bounding boxes and 15 attributes of human body were annotated.The data can be used for re-id and other tasks.

Surveillance scenes Re-ID multiple time periods multiple ages human body rectangular bounding boxes attributes annotation
208,914 Bounding Boxes – Human Body Attributes Data in Surveillance Scenes

208,914 Bounding Boxes – Human Body Attributes Data in Surveillance Scenes. The data includes indoor (shopping mall) and outdoor (street, the gate of shopping mall and square) scenes. The data includes males and females and the age distribution is from children to the elderly. In this dataset, the rectangular bounding boxes and 19 attributes of human body were annotated. The data can be used for person attributes recognition.

indoor and outdoor scenes human body bounding boxes appendage bounding boxes surveillance camera multiple age groups human body attributes
21,404 Images - Human Posture Detection Data in Home Scenes

21,404 images - human posture detection data in home scenes. The data scenes are 101 different indoor hone scenes. The gender distribution includes male and female, the age distribution is ranging from young to the elderly, the middle-aged and young people are the majorities. The data diversity includes multiple scenes, multiple time periods, multiple collecting heights, multiple human body occlusions, multiple collecting distances. For collection content, the human body postures data in different home scenes were collected, the human bodies were lying flat, lying on its side or lying on its stomach. For annotation, human body rectangular bounding boxes were annotated. The data can be used for tasks such as human body detection in home scenes.

Home scene human posture detection multiple scenes multiple time periods multiple collecting heights multiple human body occlusions multiple collecting distances human body rectangular bounding boxes
40 People – Safety Dressing Collection Data

40 People – Safety Dressing Collection Data. Each subject collects 24 videos, each video lasts about 30 seconds. The gender distribution includes male and female, the age distribution is young and middle-aged. Collecting scenes include 2 indoor scenes and 2 outdoor scenes. The collecting angles are looking down angle, looking up angle. The data diversity includes multiple scenes, multiple actions, multiple angles, multiple safety dressing equipment. The data can be used for tasks such as detection and recognition of safety dressing for power personnel.

Safety dressing indoor scene outdoor scenes multiple scenes multiple actions multiple angles multiple safety dressing equipment detection and recognition of safety dressing for power personnel
2,769 People - CCTV Re-ID Data in Europe

2,769 People – CCTV Re-ID Data in Europe. The data includes males and females, the race distribution is Caucasian, black, Asian, and the age distribution is from children to the elderly. The data diversity includes different age groups, different time periods, different cameras, different human body orientations and postures. 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.

European CCTV Re-ID different age groups different time periods different cameras different human body orientations and postures the human body rectangular bounding boxes the rectangular bounding boxes
65 People –15,204 Videos of Sports and Fitness Video Data

65 People –15,204 Videos of Sports and Fitness Video Data. The data collection scene is indoor scenes. The race distribution is Asian, black and Caucasian; the age distribution is young and middle-aged people. The collection device is IR and RGB cameras. The dataset diversity includes different races, different age groups, different shooting angles, different collection distances, different human body orientations, different costumes and various fitness actions. The data can be used for tasks such as human behavior recognition and human segmentation in fitness scenes.

Sports and fitness IR and RGB cameras human behavior recognition human segmentation fitness scenes.
189 Videos-Electric Bicycle Entering Elevator Data

189 Videos-Electric Bicycle Entering Elevator Data,the total duration is 1 hour 58 minutes 40.72 seconds. The data covers different types of elevators, different types of electric bicycles, different time periods. The data can be used for tasks such as electric bicycle detection, electric bicycle recognition .

different types of elevators different types of electric bicycles different types of non-electric bicycles different time periods
Tell Us Your Special Needs

By submitting, I agree to the Privacy Protection

0df68d21-52d4-4b2a-8277-a1a8d4ea71ed

598369a7-c513-4a84-b84c-d9b806b97c38