Videopsy: dissecting visual data in space-time

Network camera, made possible by recent advances in the integration of sensing, compression, and communication hardware, is a new video source that can be easily deployed and remotely managed. Unobtrusively located along highways, at airports, or in office buildings, such cameras can form a visual sensor network, or camera web, an extremely rich source of visual information. In its infancy today, camera web deployment will likely accelerate in the future and one can expect visual sensing devices to eventually become as ubiquitous as electric bulbs. While the capturing hardware has evolved tremendously, hardware and algorithms necessary for effective analysis and efficient communication of multi-camera data clearly lag. In this article, I overview one particular aspect of visual data analysis, namely, space-time video segmentation that is often a pre-requisite for motion estimation, video compression, event detection, scene understanding, etc. I introduce the concept of object tunnel, a 3-D surface in space-time through which a video object travels, and the associated concept of occlusion volume. I present examples of object tunnels and occlusion volumes on surveillance data that, upon further processing, may lead to automatic event detection or scene understanding. Finally, I describe challenges in extending video analysis algorithms to visual sensor networks, and I outline some possible approaches