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

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2 Author(s)
Dal Col, L. ; Dept. of Ind. Eng. & Inf. Technol., Univ. of Trieste, Trieste, Italy ; Pellegrino, F.A.

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.

Published in:

Automation Science and Engineering (CASE), 2011 IEEE Conference on

Date of Conference:

24-27 Aug. 2011