Automatic human motion analysis and action recognition in athletics videos

We present an unsupervised, automatic human motion analysis and action recognition scheme tested on athletics videos. First, four major human points are recognized and tracked using human silhouettes that are computed by a robust camera estimation and object localization method. Statistical analysis of the tracking points motion obtains a temporal segmentation on running and jump stage. The method is tested on athletics videos of pole vault, high jump, triple jump and long jump recognizing them using robust and independent from the camera motion and the athlete performance features. The experimental results indicate the good performance of the proposed scheme, even in sequences with complicated content and motion.