Trajectory based Integrated Features for Action Classification from Depth Data

Abstract

We present an approach for Human Action Recognition based on amalgamation of features from depth maps and body-joint data. This Integrated feature set consists of depth features based on gradient orientation and motion energy, in addition to features from 3D- skeleton data capturing its statistical details. Feature selection is carried out to extract a relevant set of features for action recognition. The resultant set of features are evaluated using SVM classifier. We validate our proposed method on various benchmark datasets for Action Recognition such as MSR-Daily Activity and UT-Kinect dataset. © Springer Nature Singapore Pte Ltd. 2018.