Linear Artificial Forces for Human Dynamics in Complex Contexts

In complex contexts, people need to adapt their behavior to interact with the surrounding environment to reach the intended destination or avert collisions. Motion dynamics should therefore include both social and kinematic rules. The proposed analysis aims at defining a linear dynamic model to predict future positions of different types of agents, namely pedestrians and cyclists, observing a limited number of frames. The dynamics are defined in terms of artificial potentials fields (APFs) obtained by static (e.g., walls, doors or benches) and dynamic (e.g, other agents) elements to produce attractive and repulsive forces that influence the motion. A linear combination of such forces affects the resulting behavior. We exploit the context using a semantic scene segmentation to derive static forces while the interactions between agents are defined in terms of their reciprocal physical distances. We conduct experiments both on synthetic and on subsets of publicly available datasets to corroborate the proposed model.