Computer Vision Datasets

2,341 people human action data in online conference scenes, including Asian, Caucasian, black, brown, mainly young and middle-aged people, collected a variety of indoor office scenes, covering meeting rooms, coffee shops, library, bedroom, etc.rnEach person collected 23 videos and 4 images. The videos included 23 postures such as opening the mouth, turning the head, closing the eyes, and touching the ears. The images included four postures such as wearing a mask and wearing sunglasses.

2,341 people human action data in online conference scenes, includes Asians, Caucasians, blacks, and browns. The age is mainly young and middle-aged. It collects a variety of indoor office scenes, covering meeting rooms, coffee shops, libraries, bedrooms, etc. Each person collected 11 videos, including human body behaviors such as shaking the body from side to side, eating, and stretching.

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.

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.

40 People – 3D&2D Living_Face & Anti_Spoofing Data. The collection scenes are indoor scenes and outdoor scenes. The dataset includes males and females, the age distribution is 18-57 years old. The device includes cellphone, camera, iPhone of multiple models (iPhone X or more advanced iPhone models). The data diversity includes multiple devices, multiple actions, multiple facial postures, multiple anti-spoofing samples, multiple light conditions, multiple scenes. This data can be used for tasks such as 2D Living_Face & Anti_Spoofing, 2D face recognition, 3D face recognition, 3D Living_Face & Anti_Spoofing.

2,937 People with Occlusion and Multi-pose Face Recognition Data, for each subject, 200 images were collected. The 200 images includes 4 kinds of light conditions * 10 kinds of occlusion cases (including non-occluded case) * 5 kinds of face pose. This data can be applied to computer vision tasks such as occluded face detection and recognition.rn

314,178 Images 18_Gestures Recognition Data. This data diversity includes multiple scenes, 18 gestures, 5 shooting angels, multiple ages and multiple light conditions. For annotation, gesture 21 landmarks (each landmark includes the attribute of visible and visible), gesture type and gesture attributes were annotated. This data can be used for tasks such as gesture recognition and human-machine interaction.