A Pseudo-Random Scan perspective to the motion detection paradigm

A novel motion detection method is proposed and its properties are investigated. It combines pseudo-random scanning of the image with a simple and efficient feature detector inspired from cellular automata dynamics to detect motion as noisy parts of the scanned image. The proposed method (Pseudo Random Scan combined with Clustering Coefficient - PRS+CC) provides several useful features including fast pattern recognition and can be adapted and implemented to detect motion and extract at some point relevant feature. Experimental results suggest computational efficiency when compared to traditional motion detection approaches.