Detection of non-conventional events on video scenes

This article presents a novel approach for detection of non-conventional events in videos scenes. This novel approach consists in analyzing in real-time video from a security camera to detect, segment and tracking objects in movement to further classify its movement as conventional or non-conventional. From each tracked object in the scene features such as position, speed, changes in directions and in the bounding box sizes are extracted. These features make up a feature vector. At the classification step, feature vectors generated from objects in movement in the scene are matched almost in real-time against reference feature vectors previously labeled which are stored in a database and an algorithm based on the instance-based learning paradigm is used to classify the object movement as conventional or non-conventional. Experimental results on video clips from two databases (Parking Lot and CAVIAR) have shown that the proposed approach is able to detect non-conventional events with accuracies between 77% and 82%.