3D human pose recovery from image by efficient visual feature selection
In this paper we propose a new examplar-based approach to recover 3D human poses from monocular images. Given the visual feature of each frame, pose retrieval is first conducted in the examplar database to find relevant pose candidates. Then, dynamic programming is applied on the pose candidates to recover a continuous pose sequence. We make two contributions within this framework. First, we propose to use an efficient feature selection algorithm to select effective visual feature components. The task is formulated as a trace-ratio criterion which measures the score of the selected feature component subset, and the criterion is efficiently optimized to achieve the global optimum. The selected components are used instead of the original full feature set to improve the accuracy and efficiency of pose recovery. As second contribution, we propose to use sparse representation to retrieve the pose candidates, where the measured visual feature is expressed as a sparse linear combination of the examplars in the database. Sparse representation ensures that semantically similar poses have larger probability to be retrieved. The effectiveness of our approach is validated quantitatively through extensive evaluations on both synthetic and real data, and qualitatively by inspecting the results of the real time system we have implemented.
pose recovery feature selection sparse representation motion understanding