Real-time pedestrian detection based on A hierarchical two-stage Support Vector Machine

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This Paper presents an SVM (Support Vector Machine) based real-time pedestrian detection scheme for next-generation automotive vision applications. To meet the requirement of real-time detection with high accuracy, we designed the proposed system consisting of 2-stage hierarchical SVMs. In the proposed system, most of the input data are classified by the 1st stage linear SVM and only the inputs between positive and negative hyper-plane of the linear SVM are transferred to the 2nd stage non-linear SVM. This hierarchical 2-stage classifier can be suited for various systems via controlling the amount of data processed by the 2nd stage classifier, which trades off the detection accuracy and the required system resources. To make the proposed 2nd stage non-linear SVM further appropriate for various systems, a hyper-plane approximation technique by sample pruning has been adopted. By reducing the number of required SVs (Support Vectors) using this technique and controlling the amount of data processed via the 2nd stage classifier, high precision non-linear SVM can be employed in the proposed real-time pedestrian detection system. Simulations using HOG (Histogram of Oriented Gradient) features and Daimler pedestrian dataset show the proposed system provides highly accurate classification results under the real-time constraint of application.