Binocular Full-Body Pose Recognition and Orientation Inference Using Multilinear Analysis

In this chapter, we propose an approach to full-body pose recognition and body orientation estimation using multilinear analysis. We extract low-dimensional pose and body orientation coefficient vectors by performing tensor decomposition and projection on silhouette images obtained from wide baseline binocular cameras. The coefficient vectors are then used as feature vectors in pose recognition and body orientation estimation. To do pose recognition, pose coefficient vectors obtained from synthesized pose silhouettes are used to train a family of support vector machines as pose classifiers. Using orientation coefficient vectors, a 1-D orientation manifold is learned and further used for the estimation of body orientation. Experiment results obtained using both synthetic and real image data showed that the performance of our approach is comparable to existing pose recognition approaches, and that our approach outperformed the traditional tensor-based recognition approach in the comparative test.