A cognitive surveillance system for detecting incorrect traffic behaviors

Traffic accidents, in which pedestrians are often involved, injure and kill many people yearly. One critical scene is the crosswalk, an important knocking down source due to both drivers’ and pedestrians’ incorrect behaviors. In this work, we propose a novel approach to detect and sanction these drivers’ abnormal behaviors. The multi-agent system paradigm embodies this approach, together with the use of FIPA standards, and a traffic ontology formalizes the domain knowledge. Besides developing the architecture, a simulator is also presented for generating random situations in the crosswalk scene. This system involves a first approximation in relation to the control of drivers’ abnormal behaviors in a crosswalk controlled by a traffic light, obtaining a high-scalable and flexible system which can be adapted to other domains.