Enabling real-time tracking in embedded camera networks using compressive sensing
Associate Professor Chun Tung Chou (School of Computer Science and Engineering, University of New South Wales, Sydney)
COMPUTER SCIENCE SEMINAR\
DATE: 2013-06-24 TIME: 11:00:00 - 12:00:00 LOCATION: CSIT Seminar Room, N101 ...
An embedded camera network consists of distributed video sensors interconnected by wireless links. Due to the high data rate of video sensors, it is not feasible to stream the video data to a centralised location (sink) for processing. This means that a lot of the video processing must be performed on the embedded device. However, this presents a challenge because of limited computation power on embedded platforms. For example, conventional background subtraction methods can only process a few video frames per second on embedded platforms.
In this talk, we discuss how compressive sensing can be used to address this computational bottleneck. We present a compressive sensing based background subtraction method, whose accuracy is similar to conventional methods, but is five times faster. We show that this faster background subtraction method enables real-time tracking in an embedded camera network. If time allows, I will also discuss briefly on some of my other work on using compressive sensing in wireless sensor networks.
Chun Tung Chou is an Associate Professor at the School of Computer Science and Engineering, University of New South Wales, Sydney, Australia. He received his Ph.D. from the University of Cambridge, UK. He has published over 100 articles on various topics, including, wireless mesh networks, wireless sensor networks and system identification. His current research interests are wireless sensor networks, compressive sensing and nano-communication.