Place-dependent people tracking

Abstract

People detection and tracking are important in many situations where robots and humans work and live together. But unlike targets in traditional tracking problems, people typically move and act under the constraints of their environment. The probabilities and frequencies for when people appear, disappear, walk or stand are not uniform but vary over space making human behavior strongly place-dependent. In this paper we present a model that encodes spatial priors on human behavior and show how the model can be incorporated into a people-tracking system. We learn a non-homogeneous spatial Poisson process that improves data association in a multi-hypothesis target tracker through more informed probability distributions over hypotheses. We further present a place-dependent motion model whose predictions follow the space-usage patterns that people take and which are described by the learned spatial Poisson process. Large-scale experiments in different indoor and outdoor environments using laser range data, demonstrate how both extensions lead to more accurate tracking behavior in terms of data-association errors and number of track losses. The extended tracker is also slightly more efficient than the baseline approach. The system runs in real-time on a typical desktop computer.