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Pedestrian detection is an important research field of computer vision, and is a key technology of automatic drive. One category of pedestrian detection methods is statistics learning methods. But these methods cannot guarantee that the negative samples include all real scenes in practice. And big negative data lead to a complex classifier. Compared to negative samples, positive samples are more easily to be complete. To solve the above problem, we propose a novel pedestrian detection method based on Histogram of Oriented Gradient (HOG) and support vector data description (SVDD). First, create multi-scale samples by shift window. Second, samples are transformed by HOG method to provide features for classifiers. Third, a single classifier is trained with complete positive samples. The single classifier is based on SVDD. A series of experiments show that the proposed method needs more positive samples to train the classifier compared to previous works, but the number of total samples is less than that of previous works. And the false detection of the proposed method is less than that of classical method. Keywords—histogram of oriented gradient; support vector data description; pedestrian detection; statistics learning

A Novel Pedestrian Detection Method Based on Histogram of Oriented gradient and Support Vector Data Description