Vehicle color classification under different lighting conditions through color correction

This paper presents a novel color correction technique for classifying vehicles under different lighting conditions using their colors. To reduce the lighting effects, a reference image is first selected for building the mapping function between the current frame and the reference image. With this mapping function, the color distortions between frames can be reduced to minimum. In addition to lighting changes, the effect of sun light will make the vehicle window become white and lead to the errors of vehicle classification. To reduce this effect, a window-removing task is then applied for making vehicle pixels with the same color more concentrated on the foreground region. Then, vehicles can be more accurately classified to their categories even though strong sun light casts on them. To tackle the confusion problem that some vehicle colors are too similar, e.g., deep-blue and deepgreen, a novel tree-based classifier is then designed for classifying vehicles to more detailed labels. Experimental results have proved that the proposed method is a robust, accurate, and powerful tool for vehicle classification.