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 useHistogram 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