Motion model selection in tracking humans

Abstract

The performance of many human tracking algorithms rely on accurate motion models. Due to the nature of human motion it is often difficult to determine the suitability of a chosen model. It is typically the case that over the tracking duration the characteristics of the observed motion will fit many different models. Commonly used motion models in the area of tracking include the constant position (CP), constant velocity (CV) and constant acceleration (CA) motion models. This paper applies the Kalman filtering algorithm to the problem of tracking a person's position using a finite set of different motion models. The statistical properties of the innovation sequence are used as a basis for motion model selection