Flex-SURF: A Flexible Architecture for FPGA-Based Robust Feature Extraction for Optical Tracking Systems

In this paper, we propose a novel architecture to accelerate the Speeded Up Robust Features (SURF) algorithm by the use of configurable hardware. SURF is used in optical tracking systems to robustly detect distinguishable features within an image in a scale and rotation invariant way. In its performance critical part, SURF computes convolution filters at multiple scale levels without the need to create down-sampled versions of the original image. However, the algorithm exposes a very irregular memory access pattern. We designed a configurable and scalable architecture to overcome these memory access issues without the need to use any internal block RAM resources of the FPGA. The complete detector and descriptor stage of SURF has been implemented and validated in a Virtex 5 FPGA.