Knowledge-based video compression for search and rescue robots and multiple sensor networks Conference Paper uri icon

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.

author list (cited authors)

  • Williams, C., & Murphy, R. R.

citation count

  • 2

publication date

  • May 2006

publisher