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

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 1^st stage linear SVM and only the inputs between positive and negative hyper-plane of the linear SVM are transferred to the 2^nd stage non-linear SVM. This hierarchical 2-stage classifier can be suited for various systems via controlling the amount of data processed by the 2^nd stage classifier, which trades off the detection accuracy and the required system resources. To make the proposed 2^nd 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 2^nd 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.