Searching Human Action Recognition Accuracy from Depth Video Sequences Using HOG and PHOG Shape Features

Abstract

Research on human action recognition from depth video sequences are increasing day by day due to its vast application in automatic surveillance systems, entertainment environments, and healthcare systems etc. In our project, we improve human action recognition accuracy using shape features. We use Histogram of oriented gradients (HOG) and Pyramid Histogram of oriented gradients ( PHOG) to extract shape features. The feature extraction algorithms are used to extract shape feature from dataset of different action videos. At first, depth motion maps (DMMs) are constructed from every action video. Then, the HOG and PHOG features are extracted from each DMMs. Using these features, actions are recognized by the