A discrete choice pedestrian behavior model for pedestrian detection in visual tracking systems

Different approaches to the moving object detection in multi-object tracking systems use dynamic-based models. In this paper we propose the use of a discrete choice model (DCM) of pedestrian behavior and its application to the problem of the target detection in the particular case of pedestrian tracking. We analyze real scenarios assuming to have a calibrated monocular camera, allowing a unique correspondence between the image plan and the top view reconstruction of the scene. In our approach we first initialize a large number of hypothetical moving points on the top view plan and we track their corresponding projections on the image plan by means of a simple correlation method. The resulting displacement vectors are then re-projected on the top view and pre-filtered using distance and angular thresholds. The pre-filtered trajectories are the inputs for the discrete choice behavioral filter used to decide whether the pre-filtered targets are real pedestrians or not.