Human Action Recognition Using Dynamic Time Warping and Voting Algorithm

Abstract

This paper presents a human action recognition method using Dynamic Time Warping and voting algorithms on 3D human skeletal models. In this method, human actions which are the combinations of multiple body part movements are described by feature matrices in concerning with both spatial and temporal domains. The feature matrices are created based on the spatial selection of time series of relative angles between body parts. Then, action recognition is done by applying a classifier based on the combination of Dynamic Time Warping (DTW) and a Voting algorithm to the feature matrices. Experimental results show that the performance of our action recognition method obtains high recognition accuracy and reliable computation speed in order to be applied in real time human action recognition systems.