An Integrated System for Moving Object Classification in Surveillance Videos

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 runs in 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 in different positions of the camera view. Finally, we use an automatic adaptation process to continuously update the parameters of the system so that its performance increases for...

• Type: Conference
• Year: 2008
• Where: AVSS
• Authors: Longbin Chen, Rogerio Feris, Yun Zhai, Lisa M. G. Brown, Arun Hampapur
• Keywords: Arbitrary Camera Viewpoints, AVSS 2008, Perspective Distortions, Signal Processing, Smart Surveillance Systems