Detecting moving and standing objects using cellular neural networks

Abstract

Abstract The general framework of motion detection based on discrete time samples of the moving image is defined. Four types of motion detection problem are studied. the simplest one is a model resembling the famous Hubel‐Wiesel experiment with a cat's retina for detecting the motion of an object having a given speed in a given direction. the most complicated case is the determination of the vertical and horizontal velocity components of a moving image. Various cloning template sequences are proposed for detecting different types of motion. In the sampled mode the consecutive black‐and‐white snapshots are fed to the input and to the initial state nodes of the cellular neural network respectively. After the transients have decayed, the output gives the information necessary for detecting the presence or absence of a specific motion as well as for estimating the direction and magnitude of the velocity vector. In continuous mode the sampling process is eliminated by the use of delay‐type templates. Conditions are analysed under which the detection is correct. the circuit realization of some motion detectors is discussed and the use of a programmable dual‐CNN structure is proposed.