  Human action recognition involves the characterization of human actions
through the automated analysis of video data and is integral in the development
of smart computer vision systems. However, several challenges like dynamic
backgrounds, camera stabilization, complex actions, occlusions etc. make action
recognition in a real time and robust fashion difficult. Several complex
approaches exist but are computationally intensive. This paper presents a novel
approach of using a combination of good features along with iterative optical
flow algorithm to compute feature vectors which are classified using a
multilayer perceptron (MLP) network. The use of multiple features for motion
descriptors enhances the quality of tracking. Resilient backpropagation
algorithm is used for training the feedforward neural network reducing the
learning time. The overall system accuracy is improved by optimizing the
various parameters of the multilayer perceptron network.
