Support Vector Machines (SVMs) are an estab- lished 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.
