A Video-Based Frame-Level Dataset of Badminton Player Movements for Motion Analysis

This dataset contains 884 badminton video recordings collected and organized for frame-level motion analysis and computer vision research. The dataset focuses on capturing badminton player movements during gameplay, providing a valuable resource for studies related to action recognition, pose estimation, player movement analysis, sports analytics, and machine learning applications in badminton. The videos were extracted and processed into individual frames to enable detailed frame-by-frame analysis of player actions and motion patterns. The dataset includes diverse badminton movements such as footwork, positioning, directional changes, and stroke-related actions performed under real-world playing conditions. Researchers can utilize this dataset for: Human motion analysis Sports performance evaluation Action recognition and classification Computer vision and deep learning research Pose estimation and tracking Athlete movement pattern analysis Badminton-specific AI applications Dataset Characteristics: Total Videos: 884 Data Type: Video-based badminton recordings Annotation Level: Frame-level organization Domain: Sports Analytics and Computer Vision Application Areas: Motion Analysis, Action Recognition, Pose Estimation, Machine Learning and Deep Learning. This dataset is intended to support academic research, educational purposes and the development of intelligent sports analysis systems.