The human action image and its application to motion recognition

Recognizing a person's motion is intuitive for humans but represents a challenging problem in machine vision. In this paper, we present a multi-disciplinary framework for recognizing human actions. We develop a novel descriptor, the Human Action Image (HAI), a physically-significant, compact representation for the motion of a person, which we derive from Hamilton's Action. We prove the additivity of Hamilton's Action in order to formulate the HAI and then embed the HAI as the Motion Energy Pathway of the Neuro-biological model of motion recognition. The Form Pathway is modelled using existing low-level feature descriptors based on shape and appearance. Finally, we propose a Weighted Integration (WI) methodology to combine the two pathways via statistical Hypothesis Testing using the bootstrap to do the final recognition. Experimental validation of the theory is provided on the well-known Weizmann and USF Gait datasets.