Computer Vision Datasets

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.

This large-scale vehicle re-identification dataset contains 600,000 images captured from surveillance cameras across outdoor roads, including highways, road bayonets, and urban streets, etc. The data diversity includes different cameras, multiple outdoor scenes, multiple time periods. For annotation, rectangular bounding boxes of vehicles were annotated. This dataset can be use for tasks such as vehicle re-ID, traffic monitoring, and intelligent surveillance.

This dataset includes 37,049 images of engineering and construction vehicles collected by overhead surveillance cameras. The images are cropped vehicle shots, suitable for vehicle detection, vehicle tracking, vehicle counting, and traffic monitoring AI model training.