Human action recognition using histogram of motion intensity and direction from multiple views

This study presents a human action recognition system from multi-view image sequences. The authors’ approach to human action recognition is based on an estimation of local motion from multiple camera views. The authors propose a new motion descriptor, called histogram of motion intensity and direction, to capture local motion characteristics of human activity. After image normalisation, they estimate motion flow using dense optical flow. Using regular grids, they extract local flow motion and estimate the dominant angle and the intensity of optical flow. The histogram of the dominant angle and its intensity are used as a descriptor for each sequence. After the identification of head direction, they concatenate descriptors in each view as a single feature vector from multiple-view sequences. Classification based on the proposed feature vector using support vector machine shows better performance than three-dimensional optical flow-based approaches, but with lower computational requirements. The authors evaluated action recognition on the publicly available i3DPost and the Institut de Recherche en Informatique et en Automatique (INRIA) Xmas Motion Acquisition Sequences database. Experimental results show promising state-of-the-art results and validate the advanced performance of the authors’ approach.