Color identification of vehicles plays a significant role in crime detection. In this study, a novel scheme for the color\nidentification of vehicles is proposed using the locating algorithm of regions of interest (ROIs) as well as the color\nhistogram features from still images. A coarse-to-fine strategy was adopted to efficiently locate the ROIs for various\nvehicle types. Red patch labeling, geometrical-rule filtering, and a texture-based classifier were cascaded to locate the\nvalid ROIs. A color space fusion together with a dimension reduction scheme was designed for color classification.\nColor histograms in ROIs were extracted and classified by a trained classifier. Seven different classes of color were\nidentified in this work. Experiments were conducted to show the performance of the proposed method. The average\nrates of ROI location and color classification were 98.45% and 88.18%, respectively. Moreover, the classification efficiency\nof the proposed method was up to 18 frames per second.
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