Contextual Combination of Appearance and Motion for Intersection Videos with Vehicles and Pedestrians

Object detection and classification is challenging problem for vision-based intersection monitoring since traditional motion-based techniques work poorly when pedestrians or vehicles stop due to traffic signals. In this work, we present a method for vehicle and pedestrian recognition at intersections that benefits from both motion and appearance cues in video surveillance. Vehicle and pedestrian recognition performance is compared using motion, appearance and combined cues in contextually relevant stop areas to improve recognition. Experimental evaluation shows 5% average improvement for vehicle and pedestrian recognition at two Las Vegas intersections.