Improving object detection performance with genetic programming

This paper describes three developments to improve object detection performances using genetic programming. The first investigates three features sets, the second investigates a new fitness function, and the third introduces a two phase learning method using genetic programming. This approach is examined on three object detection problems of increasing difficulty and compared with a neural network approach. The two phase GP approach with the new fitness function and the local concentric circular region features achieved the best results. The results suggest that the concentric circular pixel statistics are more effective than the square features for these object detection problems. The fitness function with program size is more effective and more efficient than without for these object detection problems and the evolved genetic programs using this fitness function are much shorter and easier to interpret. The two phase GP approach is more effective and more efficient than the single stage GP approach, and also more effective than the neural network approach on these problems using the same set of features.