Localized Temporal Representation in Human Action Recognition

Abstract

The development of automated video surveillance has grown dramatically due to the increased concern with public safety and security. An automated surveillance with reliable human activity analysis is essential. In this paper, a localized spatio-temporal representation, alongside Motion History Image (MHI), Motion Energy Image (MEI) and Binarized Statistical Image Features (BSIF), is proposed for human action recognition. In this work, the information of timestamp and ratio of colors are extracted from the silhouette of MHI template. This information is then utilized to derive a temporal representation for encoding movement dynamics. This temporal representation preserves transient information of actions. Subsequently, local descriptors are computed from MHI and MEI temporal templates via BSIF. The computed localized temporal representation is classified by using a linear SVM. The proposed system offers promising performance in human action recognition with about 90% accuracy.