Gesture Recognition Video Dataset

Current security systems rely on traditional cameras and sensors for gesture recognition. However, due to diverse gestures and complex environmental factors, there are often inaccuracies affecting reaction speed and decision accuracy. Existing solutions suffer from low recognition rates and high false alarm rates due to data homogeneity and inconsistent labeling. This dataset aims to overcome these technical bottlenecks with diverse video gesture data, meeting the needs of real-time accurate recognition. Data is collected using high-frame-rate HD cameras in varied environments to ensure scene diversity and realism. For quality control, a multi-round expert annotation and consistency verification process is designed to ensure labeling accuracy and data integrity. The annotation team is composed of computer vision and security experts with rich practical experience. Data preprocessing includes denoising, editing, and normalization, employing cutting-edge technology to ensure efficient storage. Data is stored in MP4 format, facilitating operation and access.