APRIL 30, 2009--The rapid proliferation of surveillance cameras at airports, along highways, and in other public areas makes it increasingly difficult to have humans assist in monitoring. Reliable methods are needed for autonomous video analysis, known as video analytics.
One of the most important—and difficult—goals of video analytics is to detect abnormalities or events that differ from what is considered usual, such as an abandoned package, a car traveling against traffic, or a fallen elderly person. While it is already possible to identify a simple abnormality using motion detection, such as an intruder in a restricted area, that technology does not work in more complex scenarios, such as a car traveling against dense traffic.
Researchers at the University of Sherbrooke (Sherbrooke, QC, Canada) and Boston University (Boston, MA, USA) have developed a simple, memory-light approach that has led to some surprising results. They are currently exploring implementing behavior subtraction in embedded architectures used in IP surveillance cameras. This would permit edge-based processing to reduce data flow in the network by communicating frames with unusual content only.
They are also working on extending their method to multicamera configurations. For more information, go to: http://spie.org/x34279.xml?highlight=x2410&ArticleID=x34279
-- Posted by Conard Holton, Vision Systems Design, www.vision-systems.com
