Human activity recognition and gymnastics analysis through depth imagery
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Depth imagery is transforming many areas of computer vision, such as object recognition, human detection, human activity recognition, and sports analysis. The goal of my work is twofold: (1) use depth imagery to effectively analyze the pommel horse event in men’s gymnastics, and (2) explore and build upon the use of depth imagery to recognize human activities through skeleton representation. I show that my gymnastics analysis system can accurately segment a scene based on depth to identify a ‘depth of interest’, ably recognize activities on the pommel horse using only the gymnast’s silhouette, ...