Human activity recognition based on pose points selection

A novel method for human action recognition is proposed in this paper. Traditional spatial-temporal interest point detectors are easily affected by hair, face, shadow, clothes texture or the shake of camera. Inspired by the use of points distribution information, we propose a point selection method to select representative points (denoted by the “pose points”), which use HOG human detector and contour detector to select the points on human pose edges. The pose points carry both local gradient information and global pose information. 3D-SIFT scale selection method and novel descriptors called body scale and motion intensity feature are also studied. The descriptors calculate the width scale of different levels of human body and count motion intensity of activity in five directions. The descriptors combine spatial location with the moving intensity together and are used for further classification with SVMs. Experiments have been conducted on benchmark datasets and show better performance than previous methods, which achieved 99.1% on Weizmann dataset and 95.8% on KTH dataset.