GENETIC PROGRAMMING FOR OBJECT DETECTION

In this paper an approach to improve efficiency of Object Detection problem using Genetic Programming in terms of reducing false positives and improving processing speed is presented. We developed a new fitness function taking into consideration the mean squared error (MSE) between obtained output and desired output for training purposes based on existing function which uses linear combination of Detection Rate and False Alarm Rate,. By considering a fixed step size for the sweeping window, we showed that the false alarm rate and time taken to converge was reduced drastically, at the cost of higher tolerance. By taking three data sets of different coins against different backgrounds, we experimented with this approach on data sets. We showed the effect of changing the window size and step size on efficiency. We showed that using genetic operators like mutation to a larger extent proves more favorable than the crossover operator for the purpose of selecting population of programs for subsequent generations. This approach has proved to work sufficiently well with different kinds of objects against different backgrounds. Index Terms-Genetic Programming, Fitness function, Object Detection, Detection Rate, False Alarm Rate