A Gene Expression Programming Approach for Vehicle Body Segmentation and Color Recognition

Abstract

Color recognition in vehicles is a topic widely discussed in the literature given that color is one of the key features that define a vehicle identity. Recently, the need to recognize and describe a vehicle's appearance in traffic surveillance has grown in demand, as a result of the need for efficient traffic monitoring systems. In this work, we present a different approach to recognize the predominant color of a vehicle, with minimal computational resources when compared to other methods. The goal is to segment the car body from the original image and, then, recognize the predominant color in the segmented image. To accomplish the segmentation task, we use Genetic Expression Programming (GEP) to evolve a mathematical expression to filter the original image leaving only the body of the vehicle. Another objective of this work is to create a dataset, annotated at the pixel level, for car body segmentation. Our results showed that the proposed approach was efficient for vehicle color recognition, possibly for a real-time implementation.