Homogeneity patch search method for voting-based efficient vehicle color classification using front-of-vehicle image

Color classification plays a significant role in tracking crime vehicles. Color features can be used to classify vehicle color using a front-of-vehicle image acquired from CCTV. In this paper, we define a new color homogeneity patch and propose search method that focuses on improving the discernment of features by avoiding another color distribution, not a vehicle color, which is included in the vehicle color features. In addition, we could effectively improve this method by using a voting strategy. A region of interest (ROI) that includes a bonnet is detected using predefined information about the given car and the search method is applied to ROI for selecting color homogeneity patches. The proposed approach extracts an HSV histogram for each patch, and adopts the multi-class Adaboost algorithm to classify the color of each patch. We integrate the proposed method with the voting strategy to determine the color of the vehicle. To validate the feasibility of our approach, we compare the results with a sliding window without consideration of homogeneity. Results show that the search method for homogeneity patches can be efficiently applied to recognize vehicle color.