Feature Recycling Cascaded SVM Classifier Based on Feature Selection of HOGs for Pedestrian Detection

Since to pedestrian detection in driver assistance as well as surveillance systems is a challenging task of the recent years this paper introduces a fast cascaded classifier based on linear and non-linear support vector machines (SVMs). To yield high and accurate detection rates, histogram of oriented gradients (HOGs) will be preselected by the fisher score. These features will be a basis for the training algorithm of the cascaded classifier. A non-maximum suppression algorithm will be used and evaluated in respect to reject HOG features which have a huge overlap in a joint image area. By variation of the non-maximum suppression parameter different numbers of preselected HOG features will be used to create the cascaded classifier. The different cascaded classifiers will be evaluated and compared between each other and in relation to the HOG procedure from Dalal and Triggs combined with a support vector machine.

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