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 multiple-camera 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 Wo102 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 at 25 frames/sec on the Trimedia processor. The application uses a number of standard vision algorithms as well as some improvements of our own; the details of the algorithms are not critical to the distributed system research we propose here.
Document Details
- Document Type
- Technical Report
- Publication Date
- Aug 20, 2004
- Accession Number
- ADA428740
Entities
People
- Burak Ozer
- Tiehan Lv
- Wayne Wolf
Organizations
- Princeton University