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Human bodypart classification using geodesic descriptors and random forests

A new approach to classify human body parts in depth images is proposed. The approach is based on geodesic descriptors. Such a descriptor randomly samples the local geodesic neighborhood of each depth pixel. During a training phase, a random forest classifier learns the correct body part from these descriptors. The experimental evaluation shows that we can robustly classify 19 body parts for several different poses and body proportions. We further compare our approach and the classification based on geodesic distance features to those that were used in previous works.