Fast and accurate object detection by means of recursive monomial feature elimination and cascade of SVM

Abstract

Support Vector Machines (SVMs) are an established tool for pattern recognition. However, their application to real-time object detection (such as detection of objects in each frame of a video stream) is limited due to the relatively high computational cost. Speed is indeed crucial in such applications. Motivated by a practical problem (hand detection), we show how second-degree polynomial SVMs in their primal formulation, along with a recursive elimination of monomial features and a cascade architecture can lead to a fast and accurate classifier. For the considered hand detection problem we obtain a speed-up factor of 1600 with comparable classification performance with respect to a single, unreduced SVM.