A streaming hardware architecture for real-time SIFT feature extraction

The Scale-Invariant Feature Transform (SIFT) is a feature extractor that serves as a key step in many computer-vision pipelines. Real-time operation based on a software-only approach is often infeasible, but FPGAs can be employed to parallelize execution and accelerate the application to meet latency requirements. In this study, we present a stream-based hardware acceleration architecture for SIFT feature extraction. Using a novel strategy to store pixels required for descriptor computation, the execution time needed to generate SIFT descriptors is greatly improved relative to previous designs. This strategy also enables further reduction of the execution time by introducing multiple processing elements (PEs) for computation of several SIFT descriptors in parallel. Additionally, the proposed architecture supports keypoint detection at an arbitrary number of octaves and allows for runtime configuration of various parameters. An FPGA implementation targeting the Xilinx Zynq-7045 system-on-chip (SoC) device is deployed to demonstrate the efficiency of the proposed architecture. In the target hardware, the resulting system is capable of processing images with a resolution of 1280 × 720 pixels at up to 150 FPS while maintaining modest resource utilization.