Goal-oriented and map-based people tracking using virtual force field

Estimation of people tracking may become divergent in the presence of occlusion. Since the interactions between people and environments can be mathematically modeled and probabilistically estimated, stream field based tracking provides the solution where the state of the occluded people is estimated by inferring the interactive force between the virtual goal of a person and environmental features. Such tracker suffers from high computation complexity because of the multi-hypotheses of the person's goal and feature-based map. Therefore, this paper proposes a novel virtual force field (VFF) based tracking algorithm that can be realized with a single hypothesis for the person's goal and grid-based map. The occupied grids generate repulsive forces while the person's goal generates attractive force in the virtual force field. Since the virtual force field based tracking integrates map, person, and the person's goal, the position of the person sheltered by the environment can be accurately estimated in unknown environments. Compared with the Kalman filter with constant acceleration (CA) model and stream field based algorithms, our proposed scheme significantly improves the tracking accuracy in case of occlusion.