Hardware/software co-design of embedded real-time KD-tree based feature matching systems

Abstract

Feature matching is an important step in many computational photography applications such as image stitching, 3D reconstruction and object recognition. KD-trees based Best Bin First (KD-BBF) search is one of the most widely used feature matching scheme being employed along with SIFT and SURF. The real time requirements of such computer vision applications for embedded systems put tight compute bounds on the processor. In this paper we propose a soft-core and a hardware accelerator based architecture that enables real time matching of SIFT feature descriptors for HD resolution images at 30 FPS. The proposed accelerator provides a speedup of more than 8 times over the pure software implementation.