Human motion segmentation using collaborative representations of 3D skeletal sequences

Currently, human motion analysis using three‐dimensional (3D) data creates closer awareness in computer vision with the introduction of cost‐effective Kinect or other depth cameras. This study attempts to segment a continuous 3D skeletal sequence into several disjointed sub‐sequences, each of which is corresponding to a complete action. To address this issue, the authors propose a supervised time‐series segmentation algorithm. A bidirectional propagation search scheme is employed to reach a solution. Specifically, a human skeleton is formulated as a point in multidimensional space, and a motion trajectory is further represented as a sequence. Each training action sequence serves as an atom in a dictionary, which is adopted by an l2‐regularised collaborative representation classifier. Based on the fact that the reconstruction error of the collaborative representation measures the similarity between a test sub‐sequence and training sequences, they utilise its variation over time to capture action transition. Cut point detection and sub‐sequence recognition are simultaneously achieved. Experiments on the authors’ recorded 3D skeletal sequences demonstrate that the proposed algorithm outperforms existing human motion segmentation techniques. Their algorithm is capable of extending to segment various dimensional sequences. This extensibility is validated by synthetic signal segmentation experiments