UNDERSTANDING OBJECT RELATIONS IN TRAFFIC SCENES

An autonomous vehicle has to be able to perceive and understand its environment. At perception level objects are detected and classified using raw sensory data, while at situation interpretation level high-level object knowledge, like object relations, is required. In order to make a step towards bridging this gap between low-level perception and scene understanding we combine computer vision models with the probabilistic logic formalism Markov logic. The proposed approach allows for joint inference of object relations between all object pairs observed in a traffic scene, explicitly taking into account the scene context. Experimental results based on simulated data as well as on automatically segmented traffic videos from an on-board stereo camera platform are provided.