A traffic surveillance system using dynamic saliency map and SVM boosting

This paper proposes a traffic surveillance system that can efficiently detect an interesting object and identify vehicles and pedestrians in real traffic situations. The proposed system consists of a moving object detection model and an object identification model. A dynamic saliency map is used for analyzing dynamics of the successive static saliency maps, and can localize an attention area in dynamic scenes to focus on a specific moving object for traffic surveillance purposes. The candidate local areas of a moving object are followed by a blob detection processing including binarization, morphological closing and labeling methods. For identifying a moving object class, the proposed system uses a hybrid of global and local information in each local area. Although the global feature analysis is a compact way to identify an object and provide a good accuracy for non-occluded objects, it is sensitive to image translation and occlusion. Therefore, a local feature analysis is also considered and combined with the global feature analysis. In order to construct an efficient classifier using the global and local features, this study proposes a novel classifier based on boosting of support vector machines. The proposed object identification model can identify a class of moving object and discard unexpected candidate area which does not include an interesting object. As a result, the proposed road surveillance system is able to detect a moving object and identify the class of the moving object. Experimental results show that the proposed traffic surveillance system can successfully detect specific moving objects.