Spatio-temporal fastmap-based mapping for human action recognition

Abstract

This paper presents a simple and efficient method for action recognition based on the learning of an explicit representation for an intrinsic dynamic shape manifold of human action. The proposed model relies on a short temporal set of FastMap dimensionality reduction-based technique for embedding a sequence of raw moving silhouettes, associated to an action video into a low-dimensional space, in order to characterize the spatio-temporal property of the action, as well as to preserve much of the geometric structure. The objective is to provide a recognition method that is both simple, fast and applicable in many scenarios. Moreover, we demonstrate the robustness of our method to partial occlusion, deformation of shapes, significant changes in scale and viewpoint, irregularities in the performance of an action, and low-quality video.