Sensor network based vehicle classification and license plate identification system
INSS'09 Proceedings of the 6th international conference on Networked sensing systems
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Wireless, battery-powered camera networks are becoming of interest for surveillance and monitoring applications. The computational power of these platforms is often limited in order to reduce energy consumption. Among the visual tasks that the onboard processor may be required to perform, motion analysis is one of the most basic and relevant. Knowledge of the direction of motion and velocity of a moving body may be used to take actions such as sending an alarm or triggering other camera nodes in the network. We present a fast algorithm for identifying moving areas in an image and computing the average velocity in such areas. The algorithm, which was implemented and tested on a Crossbow Stargate embedded platform, is comprised of three stages. First, local differential measurements are used to determine an initial labeling of image blocks. A total least squares approach is proposed, with fast implementation inspired by the work of Benedetti and Perona. Then, belief propagation is used to impose spatial coherence and resolve aperture effect inherent in textureless areas. Finally, the velocity of the resulting blobs is estimated via least squares regression. A detailed analysis of timing and power consumption characteristics of this algorithm is also presented.