Genetic programming for multiple class object detection

Abstract

We describe an approach to the use of genetic progreimming for object detection problems in which the locations of small objects of multiple classes in lau-ge pictures must be found. The evolved programs use a feature set computed from a square input field leirge enough to contain each of objects of interest and are applied, in moving window fashion, over the large pictures in order to locate the objects of interest. The fitness function is based on the detection rate and the false alarm rate. We have tested the method on three object detection problems of increasing difficulty with four different classes of interest. On pictures of easy and medium difficulty all objects axe detected with no false alajms. On difficult pictures there are still significant numbers of errors, however the results are considerably better than those of a neural network based program for the same problems.