Recognizing Human Actions Using Silhouette-based HMM
This paper addresses the problem of silhouette-based human action modeling and recognition, specially when the number of action samples is scarce. The first step of the proposed system is the 2D modeling of human actions based on motion templates, by means of Motion History Images (MHI). These templates are projected into a new subspace using the Kohonen Self Organizing feature Map (SOM), which groups viewpoint (spatial) and movement (temporal) in a principal manifold, and models the high dimensional space of static templates.The next step is based on the Hidden Markov Models (HMM) in order to track the map behavior on the temporal sequences of MHI. Every new MHI pattern is compared with the features map obtained during the training. The index of the winner neuron is considered as discrete observation for the HMM. If the number of samples is not enough, a sampling technique, the Sampling Importance Resampling (SIR) algorithm, is applied in order to increase the number of observations for the HMM. Finally, temporal pattern recognition is accomplished by a Maximum Likelihood (ML) classifier. We demonstrate this approach on two publicly available dataset: one based on real actors and another one based on virtual actors.