An Integrated System for Moving Object Classification in Surveillance Videos

摘要

Moving object classification in far-field video is a key component of smart surveillance systems. In this paper, we propose a reliable system for person-vehicle classification which works well in challenging real-word conditions, including the presence of shadows, low resolution imagery, perspective distortions, arbitrary camera viewpoints, and groups of people. Our system runsin real-time (30Hz) on conventional machines and has low memory consumption. We achieved accurate results by relying on powerful discriminative features, including a novel measure of object deformation based on differences of histograms of oriented gradients. We also provide an interactive user interface, enabling users to specify regions of interest for each class and correct for perspective distortions by specifying different sizes indifferent positions of the camera view. Finally, we use anautomatic adaptation process to continuously update the parameters of the system so that its performance increases for a particular environment. Experimental results demonstrate the effectiveness of our system in standard dataset and a variety of video clips captured with our surveillance cameras.