Machine learning for people detection in guidance functionality of enabling health applications by means of cascaded SVM classifiers

Electronic assistance devices for visually impaired people often require pedestrian detection functionality. Due to the fact that these devices have only low computational processing capabilities, it is necessary to use realtime algorithms that meet these requirements.We thus propose application of a cascaded support vector machine (SVM) classifier with linear and combined cascading of SVMs based on histogram of oriented gradients (HOGs) and Fisher score preselection. Proposed algorithms are evaluated on the basis of the INRIA database and compared to the state of the art procedure with HOG-features in combination with a standard SVM.