Fast Unconstrained Vehicle Type Recognition with Dual-Layer Classification

This paper tackles the problem of vision-based vehicle type recognition, which aims at outputting a semantic label for the given vehicle. Most existing methods operate on a similar situation where vehicle viewpoints are not obviously changed and the foreground regions can be well segmented to extract texture, edge or length-width ratio. However, this underlying assumption faces severe challenges when the vehicle viewpoint varies apparently or the background is clutter. Thus we propose a dual-layer framework that can jointly handle the two challenges in a more natural way. In the training stage, each viewpoint of each type of vehicles is denoted as a sub-class, and we treat a pre-divided region of images as a sub-sub-class. In the first layer, we train a fast Exemplar-LDA classifier for each sub-sub-class. In the second layer of the training stage, all the Exemplar-LDA scores are concatenated for the consequent training of each sub-class. Due to introducing Exemplar-LDA, our approach is fast for both training and testing. Evaluations of the proposed dual-layer approach are conducted on challenging non-homologous multi-view images, and yield impressive performance.