Tracking and understanding moving pedestrian behaviors is of major concern for a growing number of applications. Classical approaches either consider the two problems separately or treat them simultaneously while relying on limited context-based graphical models. In this paper, we present an approach to tackle both the problems conjointly based on richer contextual information issued from agent-based behavioral simulators which aim to realistically reproduce human behaviors within complex environments. We focus on the special case of a single target and experimentally show that the proposed approach manages to track a single pedestrian with complex behavior even in case of long periods of occlusion.