Computation of Rotation Local Invariant Features using the Integral Image for Real Time Object Detection

Abstract

We present a framework for object detection that is invariant to object translation, scale, rotation, and to some degree, occlusion, achieving high detection rates, at 14 fps in color images and at 30 fps in gray scale images. Our approach is based on boosting over a set of simple local features. In contrast to previous approaches, and to efficiently cope with orientation changes, we propose the use of non-Gaussian steerable filters, together with a new orientation integral image for a speedy computation of local orientation