Enhanced semantic descriptors for functional scene categorization

In this work we present a novel approach which combines semantic information with low level features extracted from a complex video scene. The proposed method for video scene understanding relies on a bag-of-words approach, in which, typically, visual words contain information of local motion, but information regarding what generated such motion is discarded. Instead, in our framework, the semantic information is embedded in the visual words and it allows to automatically obtain semantic categorization of the scene. We show the effectiveness of our method in a traffic analysis scenario: in this case, two main semantic classes, pedestrians and vehicles, are discovered.