Live Demonstration: A Real-Time Event-Based Fast Corner Detection Demo Based on FPGA

作者：Min Liu, Wei-Tse Kao, Tobi Delbrück · 年份：2019 · DOI：10.1109/cvprw.2019.00212 · 被引用次数：8 · 研究领域：Advanced Memory and Neural Computing、CCD and CMOS Imaging Sensors、Neuroscience and Neural Engineering

Corner detection is widely used as a pre-processing step for many computer vision (CV) problems. It is well studied in the conventional CV community and many popular methods are still used nowadays such as Harris, FAST and SIFT. For event cameras like Dynamic Vision Sensors (DVS), similar approaches also have been proposed in recent years. Two of them are event-based harris(eHARRIS) and event-based FAST (eFAST). This demo presents our recent work in which we implement eFAST on MiniZed FPGA. The power consumption of the whole system is less than 4W and the hardware eFAST consumes about 0.9W. This demo processes at least 5M events per second, and achieves a power-speed improvement factor product of more than 30X compared with CPU implementation of eFAST. This embedded component could be suitable for integration to applications such as drones and autonomous cars that produce high event rates.