Vehicle feature extraction by patch-based sampling

Abstract

In modern traffic surveillance, computer vision methods are often employed to detect vehicles of interest because of the rich information content contained in an image. In this paper, we propose an efficient method for extracting the boundary of vehicles free from their moving cast shadows and reflective regions. The extraction method is based on the hypothesis that regions of similar texture are less discriminative, disregarding intensity differences between the vehicle body and the cast shadow or reflection on the vehicle. In this novel algorithm, a united likelihood map that based on the relationship of texture, luminance and chrominance of each pixel is initially constructed. Subsequently, a foreground mask is constructed by applying morphological operations. Vehicles can be successfully extracted and different vehicle components can be efficiently distinguished by the related autocorrelation index within the vehicle mask.