Synthesis and recognition of biological motion patterns based on linear superposition of prototypical motion sequences

The linear combination of prototypical views has been shown to provide a powerful method for the recognition and analysis of images of three-dimensional stationary objects. We present preliminary results on an extension of this idea to video sequences. For this extension, the computation of correspondences in space-time turns out to be the central theoretical problem, which we solve with a new correspondence algorithm. Using simulated images of biological motion we demonstrate the usefulness of the superposition of prototypical sequences for the synthesis of new video sequences, and for the analysis and recognition of actions. Our method permits to impose a topology over the space of video sequences of action patterns. This topology is more complicated than a linear space. We present a new method that is based on the structural risk minimization principle of statistical learning theory, which permits to exploit this knowledge about the topology of the pattern space for recognition.