Human Action Recognition System using Good Features and Multilayer Perceptron Network
This addresses the problem of robust real-time action recognition for smart computer vision systems, but it is incremental as it builds on existing feature and neural network methods.
The paper tackled human action recognition in video by combining good features with iterative optical flow for feature vectors and using a multilayer perceptron network for classification, resulting in improved system accuracy through parameter optimization.
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.