Visual categorization is one of a key function in the next generation of a driving assist system, which is expected to reduce a traffic accident. This paper proposes a high performance visual categorization method, which is based on Feature Accelerated Segment Test (FAST) feature point detectors, Histograms of Oriented Gradients (HOG) feature descriptors and Bag-of-Keypoints (BoK). Each feature descriptors were orthogonalized by applying the Principal Component Analysis (PCA) to reduce the size of dimension. As a result, our proposed method has achieved the recognition rate of 69.5% and the performance of 43.1 ms on a PC in order to categorize one object in an image into traffic related categories, i.e. pedestrians, cars, bikes, bicycles, and so on. The comparison with conventional methods will be also discussed.
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