Distributed Real-Time Embedded Video Processing

Abstract: The embedded systems group at Princeton University is building a distributed system for real-time analysis of video from multiple cameras. Most work in multiplecamera video systems relies on centralized processing. However, performing video computations at a central server has several disadvantages: it introduces latency that reduces the response time of the video system; it increases the amount of buffer memory required; and it consumes network bandwidth. These problems cause centralized video processing systems to not only provide lower performance but to use excess power as well. A deployable multi-camera video system must perform distributed computation, including computation near the camera as well as remote computations, in order to meet performance and power requirements. Smart cameras combine sensing and computation to perform real-time image and video analysis. A smart camera can be used for many applications, including face recognition and tracking. We have developed a smart camera system [Wol02] that performs real-time gesture recognition. This system, which currently runs on a Trimedia TM-100 VLIW processor, classifies gestures such as walking, standing, waving arms. It currently runs a