Understanding human motion patterns

Abstract

This paper addresses the recognition of human motion patterns. We represent the human body structure in the silhouette by a stick figure model. The human motion, thus, can be recorded as a sequence of the stick figure parameters, which can be used as input of a motion pattern analyzer. The recognition of human motion pattern is divided into two stages. In the first stage, a model-driven approach is used to track human motions. This is, in fact, finding the stick figure model which represents the human silhouette in each frame. In the second stage, a BP neural network classifies motions of the stick figures into three categories: walking, running and other motions. We transform the time sequence of stick figure parameters into Fourier domain by DFT, and use only the first four Fourier components as the input of the neural network.