Supervised Learning for Human Action Recognition from Multiple Kinects

Abstract

The research of Human Action Recognition (HAR) has made a lot of progress in recent years, and the research based on RGB images is the most extensive. However, there are two main shortcomings: the recognition accuracy is insufficient, and the time consumption of the algorithm is too large. In order to improve these issues our project attempts to optimize the algorithm based on the random forest algorithm by extracting the features of the human body 3D, trying to obtain more accurate human behavior recognition results, and can calculate the prediction results at a lower time cost. In this study, we used the 3D spatial coordinate data of multiple Kinect sensors to overcome these problems and make full use of each data feature. Then, we use the data obtained from multiple Kinects to get more accurate recognition results through post processing.