Summary: People Tracking with Human
Motion Predictions from Social Forces
Matthias Luber Johannes A. Stork Gian Diego Tipaldi Kai O. Arras
Abstract-- For many tasks in populated environ-
ments, robots need to keep track of current and future
motion states of people. Most approaches to people
tracking make weak assumptions on human motion
such as constant velocity or acceleration. But even
over a short period, human behavior is more complex
and influenced by factors such as the intended goal,
other people, objects in the environment, and social
rules. This motivates the use of more sophisticated
motion models for people tracking especially since
humans frequently undergo lengthy occlusion events.
In this paper, we consider computational models de-
veloped in the cognitive and social science communi-
ties that describe individual and collective pedestrian
dynamics for tasks such as crowd behavior analysis.
In particular, we integrate a model based on a social
force concept into a multi-hypothesis target tracker.