DPM revisited: Utilizing root-part spatial distribution for vehicle viewpoint estimation

Vehicle viewpoint estimation plays an important role for intelligent transportation systems. We present an effective vehicle viewpoint estimation technique by utilizing the spatial location information of root and part objects detected in vehicle images via deformable part-based model (DPM). The viewpoint-aware spatial distribution of each part relative to the root is learned using the Gaussian mixture model. The discriminative capability of each part for each viewpoint is then estimated through measuring the Kullback Leibler divergence between pairwise viewpoint-aware spatial distributions. The discriminative information is finally used to compute the likelihood that the detected vehicle belongs to each viewpoint. Experimental results on a benchmark dataset demonstrate the superior performance of the proposed vehicle viewpoint estimation technique.