Computer Vision Datasets

This dataset collected from 2,341 people in online conference scenarios. Participants 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. This dataset can be applied to tasks including human activity recognition, human action recognition, human behavior analysis, human pose estimation, and gesture recognition.

This dataset collected from 2,341 people in online conference scenarios, Participants include Asian, Caucasian, Black, Brown individuals, mainly young and middle-aged people. Data was collected from a variety of indoor office scenes, covering meeting rooms, coffee shops, library, bedroom, etc. Each participant contributed 23 videos covering actions like opening the mouth, turning the head, closing the eyes, and touching the ears, with 4 images featuring variations such as wearing a mask and sunglasses.This dataset is suitable for tasks such as human activity recognition, human action recognition, human pose estimation, gesture recognition, mask detection, and AI applications in video conferencing and virtual meetings.

This dataset contains 2,418 fight videos, covering indoor scenes (restaurant, arena, bus, boxing room, etc.) and outdoor scenes (road, crosswalk, lawn, etc.). The data covers multiple scenes, different time, multiple weather distribution. The data can be used for tasks such as fight detection, action recognition, and behavior analysis tasks in computer vision.

This dataset contains 15,204 videos from 65 people collected from indoor scenes. The race distribution is Asian, black and Caucasian; the age distribution from young to 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 fitness action recognition, human activity recognition, pose estimation, and human segmentation tasks.

This dataset diversity includes multiple scenes, light conditions, ages, shooting angles, and poses. In terms of annotation, we adpoted instance segmentation annotations on human body. 22 landmarks were also annotated for each human body. This dataset is suitable for training and benchmarking AI models in human body analysis, gesture recognition, and multi-person detection.

This dataset includes 9,000 images from 180 individuals, each labeled with 21 hand gesture landmarks along with attributes such as visibility, gesture type, gender, age, nationality, and vehicle type. Captured across diverse driving environments and time periods, this data supports research and model training in gesture recognition, pose estimation, and driver behavior analysis. It is especially useful for developing driver monitoring systems (DMS) and in-cabin interaction technologies. All data is compliant with GDPR, CCPA, and PIPL regulations and has been quality verified by leading AI companies.

This dataset features 30 million high-resolution video clips sourced from authorized and legally compliant channels. Each video offers exceptional clarity, color accuracy, and scene diversity across various environments and themes. All clips include clearly documented copyright ownership and commercial usage rights, making them safe and reliable for both academic research and business applications. The dataset is ideal for computer vision tasks such as video classification, action recognition, multimodal learning, object tracking, and content generation. Whether you're training deep learning models or sourcing clean, scalable visual data, this video dataset offers a comprehensive, large-scale solution.

This dataset contains 10,173 videos capturing cellphone usage behavior in both indoor and outdoor environments. The dataset covers diverse scenes, multiple shooting angles and various video resolutions. This dataset can be used for tasks such as cellphone playing behavior detection, cellphone interaction recognition, human activity analysis, and other related AI applications.

This dataset contains 10,464 videos capturing mobile phone calling behavior in both indoor and outdoor environments. The data covers multiple scenes, multiple shooting angles and various video resolutions. The data can be used for tasks such as calling behavior detection, phone call recognition, human activity analysis, and related AI applications.