Vehicle detection and recognition for automated guided vehicle

In this paper, we present a two-stage vision-based approach to detect front and rear vehicle views in road scene images using eigenspace and a support vector machine for classification. The first stage is hypothesis generation (HG), in which potential vehicles are hypothesized. During the hypothesis generation step, we use a vertical, horizontal edge map to create potential regions where vehicles may be present. The second stage is hypothesis verification (HV). In this stage, all hypotheses are verified by using a principle component analysis (PCA) for feature extraction and a support vector machine (SVM) for classification, which is robust for both front and rear vehicle view detection problems. Our methods have been tested on different real road images and show very good performance.