Human Action Recognition System using Good Features and Multilayer Perceptron Network

TL;DR

This paper proposes a human action recognition system that combines good features and optical flow with an MLP classifier, improving accuracy and robustness in challenging video scenarios.

Contribution

It introduces a novel combination of feature extraction and neural network training techniques for efficient and accurate human action recognition.

Findings

Enhanced tracking quality with multiple motion features

Reduced training time using resilient backpropagation

Improved system accuracy through parameter optimization

Abstract

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.