Authors: Matthias Luber, Johannes A. Stork, Gian Diego Tipaldi, and Kai O. Arras
Abstract:
For many tasks in populated environments, robots need to keep track of present and future motion states of people. Most approaches to people tracking make weak assumptions on human motion such as constant velocity and direction. But even over a short period, human motion behavior is more complex and influenced by factors such as an intended goal, other people, objects in the environment, or social rules. Therefore, more sophisticated motion models are highly desirable especially since people frequently undergo lengthy occlusion events.
For the study of crowd behavior or evacuation dynamics, computational models that describe individual and collective pedestrian dynamics have been developed in e.g. the social psychology community. In this paper, we make use of such a model for the purpose of people tracking. Concretely, we integrate a pedestrian dynamics model based on social forces into a multi-hypothesis target tracker. We show how the re ned motion predictions translate into more informed probability distributions over hypotheses and nally into a more robust tracking behavior and better occlusion handling. In experiments in indoor and outdoor environments with data from a laser range nder, the social force model leads to more accurate tracking with up to two times fewer data association errors.
