Vehicle detection using normalized color and edge map

This paper presents a novel vehicle detection approach for detecting vehicles from static images using color and edges. Different from traditional methods which use motion features to detect vehicles, this method introduces a new color transform model to find important "vehicle color" from images for quickly locating possible vehicle candidates. Since vehicles have different colors under different lighting conditions, there were seldom works proposed for detecting vehicles using colors. This paper proves that the new color transform model has extreme abilities to identify vehicle pixels from backgrounds even though they are lighted under various illumination conditions. Each detected pixel corresponds to a possible vehicle candidate. Then, two important features including edge maps and coefficients of wavelet transform are used for constructing a multi-channel classifier to verify this candidate. According to this classifier, we can perform an effective scan to detect all desired vehicles from static images. Since the color feature is first used to filter out most background pixels, this scan can be extremely quickly achieved. Experimental results show that the integrated scheme is very powerful in detecting vehicles from static images. The average accuracy of vehicle detection is 94.5%.