Contour graph based human tracking and action sequence recognition

This paper introduces a new framework for human contour tracking and action sequence recognition. Given a gallery of labeled human contour sequences, we define each contour as a "word" and encode all of them into a contour dictionary. This dictionary will be used to translate the video. To this end, a contour graph is constructed by connecting all the neighboring contours. Then, the motion in a video is viewed as an instance of random walks on this graph. As a result, we can avoid explicitly parameterizing the contour curves and modeling the dynamical system for contour updating. In such a work setting, there are only a few state variables to be estimated when using sequence Monte Carlo (SMC) approach to realize the random walks. In addition, the walks on the graph also perform sequence comparisons implicitly with those in the predefined gallery, from which statistics about class label is evaluated for action recognition. Experiments on diving tracking and recognition illustrate the validity of our method.