A 127 fps in full HD accelerator based on optimized AKAZE with efficiency and effectiveness for image feature extraction

Visual feature extraction is a fundamental technique in vision-based application. This paper proposes an effective and efficient VLSI architecture based on optimized accelerated KAZE (AKAZE) for real-time feature extraction. AKAZE is a new feature detection algorithm with strong robustness for object recognition. To extract feature more robustly and reduce hardware resource, a two-dimensional pipeline array named Loop-Snake Architecture is presented. It takes advantage of computational similarity in different octaves and provides flexibility in precision-speed tradeoff on the fly. Furthermore, Polar Local Difference Binary descriptor and the corresponding structure are proposed to greatly reduce the memory bandwidth requirement and improve the speed. The experimental results indicate the optimized algorithm keeps the same accuracy compared with the original algorithm. The whole hardware system achieves 127fps in 1080p resolution at 200 MHz frequency. The throughput is twice faster than the state-of-the-art solutions.