Multiple Agents Representation Using Motion Fields

Abstract

Providing reliable descriptions of the agents in a video scene is an essential task in many applications, such as surveillance. However, most works focus solely on the characterization of pedestrians, which is not sufficient to describe complex scenes, where a variety of vehicles (e.g., bikes and cars) are also present. In this work we address this limitation and propose a framework based on switching motion fields to efficiently characterize the different agents in a scene. Our method achieves a balanced accuracy of 91.9% on the identification of bikers and pedestrian classes on three challenging scenarios, and provides comprehensive information about their behaviors.