Simulation-Based Behavior Tracking of Pedestrians in Partially Observed Indoor Environments

Tracking and understanding moving pedestrian behaviors is of major concern for a growing number of applications. This problem, known as difficult, is more complex when the considered environment is not fully under sensory coverage. Classical approaches either focus on location estimation or attempt to build the relationship between possible activities in the environment and reason on it, which may turn out to be inadequate. In this paper, we propose an approach based on behavioral models from the situated artificial intelligence field, which aim to realistically reproduce human behaviors within complex environments. We focus on the special case of a single target and experimentally show that we are performing well even in case of long periods of occlusion.