SIFT Hardware Implementation for Real-Time Image Feature Extraction

This paper introduces a high-speed all-hardware scale-invariant feature transform (SIFT) architecture with parallel and pipeline technology for real-time extraction of image features. The task-level parallel and pipeline structure are exploited between the hardware blocks, and the data-level parallel and pipeline architecture are exploited inside each block. Two identical random access memories are adopted with ping-pong operation to execute the key point detection module and the descriptor generation module in task-level parallelism. With speeding up the key point detection module of SIFT, the descriptor generation module has become the bottleneck of the system's performance; therefore, this paper proposes an optimized descriptor generation algorithm. A novel window-dividing method is proposed with square subregions arranged in 16 directions, and the descriptors are generated by reordering the histogram instead of window rotation. Therefore, the main orientation detection block and descriptor generation block run in parallel instead of interactively. With the optimized algorithm cooperating with pipeline structure inside each block, we not only improve the parallelism of the algorithm, but also avoid floating data calculation to save hardware consumption. Thus, the descriptor generation module leads the speed almost 15 times faster than a recent solution. The proposed system was implemented on field programmable gate array and the overall time to extract SIFT features for an image having 512x512 pixels is only 6.55 ms (sufficient for real-time applications), and the number of feature points can reach up to 2900.