Abstract
Video surveillance of large facilities, such as airports, rail stations, and casinos, is developing rapidly. Cameras installed at such locations often overlook large crowds, which makes problems such as activity and scene understanding very challenging. Traditional activity understanding techniques, which rely on input from lower level processing units dealing with background subtraction, human detection, and tracking, are unable to cope with frequent occlusions in such scenes. We propose a novel spatiotemporal segmentation and activity recognition framework that bypasses these commonly used lowβlevel modules. We model each local spatiotemporal patch as a dynamic texture. Using a suitable distance metric to compare two local patches based on their estimated dynamic texture parameters, we segment a video into spatiotemporal regions that show similar motion patterns. We are also able to temporally stitch together local regions to form activity streamlines and represent each streamline by its constituent dynamic textures. This allows us to seamlessly perform activity recognition without explicitly detecting individuals in the scene. We demonstrate our framework on multiple datasets and show favorable results compared with the state of the art. Β© 2009 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 19, 80β90, 2009.