VEHICLE ORIENTATION ANALYSIS USING EIGEN COLOR, EDGE MAP, AND NORMALIZED CUT CLUSTERING

This paper proposes a novel approach for estimating vehicles' orientations from still images using "eigen color" and edge map through a clustering framework. To extract the eigen color, a novel color transform model is used for roughly segmenting a vehicle from its background. The model is invariant to various situations like contrast changes, background, and lighting. It does not need to be re-estimated for any new vehicles. In this eigen color space, different vehicle regions can be easily identified. However, since the problem of object segmentation is still ill-posed, only with this model, the shape of a vehicle cannot be well extracted from its background and thus affects the accuracy of orientation estimation. In order to solve this problem, the distributions of vehicle edges and colors are then integrated together to form a powerful but high-dimensional feature space. Since the feature dimension is high, the normalized cut spectral clustering (Ncut) is then used for feature reduction and orientation clustering. The criterion in Ncut tries to minimize the ratio of the total dissimilarity between groups to the total similarity within the groups. Then, the vehicle orientation can be analyzed using the eigenvectors derived from the Ncut result. The proposed framework needs only one still image and is thus very different to traditional methods which need motion features to determine vehicle orientations. Experimental results reveal the superior performances in vehicle orientation analysis.