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. Each 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.

This dataset contains 314,178 images of 18 gestures. This data diversity includes multiple scenes, 5 shooting angels, multiple ages and multiple light conditions. For annotation, each gesture is annotated with 21 landmarks (including the attribute of visible and invisible), gesture type and gesture attributes were also annotated. This dataset can be used for tasks such as gesture recognition, human-machine interaction, computer vision, and gesture analysis applications.

This dataset contains 180,717 images of sign language gestures, including 41 static gestures and 95 dynamic gestures. The data was captured from multiple scenes, multiple photographic angles and multiple light conditions. In terms of data annotation, each gesture is annotated with 21 landmarks, gesture types and attributes. This dataset can be used for tasks such as AI-based gesture recognition, sign language translation, human-computer interaction.

This dataset contains 50,356 human images, The data covers diverse scenes, ages, races, poses, and appendages.In terms of annotation, each human body have 18 landmarks, with segmentation for the body and appendages. This dataset can be used for tasks such as human pose estimation, body part segmentation, action recognition.

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.

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 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.

The Driver Gesture Recognition Dataset contains recordings from 1,334 participants across diverse age groups, time periods and multiple gestures. Each person performed 18 static gestures(such as fist-clenching gestures and heart-to-heart gestures) and 23 dynamic gestures(including index finger clicks and two-finger clicks). In terms of acquisition equipment, visible light and infrared binocular cameras are used. This dataset is ideal for tasks such as driver monitoring, gesture recognition in vehicles, human-computer interaction, and driver assistance systems.