Neurally inspired object tracking system

Abstract

Object tracking is useful in applications like computer-aided medical diagnosis, video editing, visual surveillance etc. Commonly used approaches usually involve the use of filter (e.g. Kalman filter) to predict the location of the object in next image frame. Such approaches actually borrow ideas from signal theory and are limited to applications where dynamic model is known. In this paper, a flexible and reliable estimation algorithm using wavelet network (or wavenet) is proposed to build an object tracking system. This system simulates the perception of motion that occurs in primates. Neural-based filters will be used for color, shape and motion analysis. Experimental results show that object can be tracked accurately without fixing any dynamic model compare with commonly used Kalman filter.