Vehicle Color Classification Under Different Lighting Conditions Through Color Correction

This paper presents a novel vehicle color classification technique for classifying vehicles into seven categories under different lighting conditions via color correction. First, to reduce lighting effects, a mapping function is built to minimize the color distortions between frames. In addition to color distortions, the effect of specular highlights can also make the window of a vehicle appear white and degrade the accuracy of vehicle classification. To reduce this effect, a window-removal task is performed to make vehicle pixels with the same color more concentrated on the analyzed vehicle. Thus, a vehicle can be more accurately classified into its corresponding category even when it is shone by strong sunlight. One major problem in vehicle color classification is that there are many shade colors; for example, white versus silver and black versus navy. Traditional methods lack the ability to classify vehicles with shade colors because a wrong classifier is designed by putting vehicles with the same label together even though their chromatic attributes are different. To treat this problem, a novel tree-based classifier is designed for classifying vehicles into chromatic/nonchromatic classes with their nonchromatic strengths and then into detailed color classes with their color features. The separation can significantly improve the accuracy of vehicle color classification even that vehicles are with various shade colors and captured under different lighting conditions.