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Proceedings Paper

Knowledge-based video compression for search and rescue robots and multiple sensor networks
Author(s): Chris Williams; Robin R. Murphy
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Paper Abstract

Robot and sensor networks are needed for safety, security, and rescue applications such as port security and reconnaissance during a disaster. These applications rely on real-time transmission of images, which generally saturate the available wireless network infrastructure. Knowledge-based compression is a method for reducing the video frame transmission rate between robots or sensors and remote operators. Because images may need to be archived as evidence and/or distributed to multiple applications with different post processing needs, lossy compression schemes, such as MPEG, H.26x, etc., are not acceptable. This work proposes a lossless video server system consisting of three classes of filters (redundancy, task, and priority) which use different levels of knowledge (local sensed environment, human factors associated with a local task, and relative global priority of a task) at the application layer of the network. It demonstrates the redundancy and task filters for a realistic robot search scenario. The redundancy filter is shown to reduce the overall transmission bandwidth by 24.07% to 33.42%, and, when combined with the task filter, reduces overall transmission bandwidth by 59.08% to 67.83%. By itself, the task filter has the capability to reduce transmission bandwidth by 32.95% to 33.78%. While knowledge-based compression generally does not reach the same levels of reduction as MPEG, there are instances where the system outperforms MPEG encoding.

Paper Details

Date Published:
PDF: 15 pages
Proc. SPIE 6248, Wireless Sensing and Processing, 624811; doi: 10.1117/12.685051
Show Author Affiliations
Chris Williams, Univ. of South Florida (United States)
Robin R. Murphy, Univ. of South Florida (United States)


Published in SPIE Proceedings Vol. 6248:
Wireless Sensing and Processing
Raghuveer M. Rao; Sohail A. Dianat; Michael D. Zoltowski, Editor(s)

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