Overview on Interest Point Detection Over 40 Year’s Development: A Review

Interest point detection (IPD) is one of the most fundamental studies in computer vision and other communities. The methods for IPD have evolved from early simple mathematical model-based to current artificial intelligence (AI)-based methods, from early 2-D gray image-based to 3-D point cloud data-based methods, and from early personal computer (PC)-based to field programmable gate array (FPGA) chip-based onboard methods. Many scholars are very concerned about these methods’ advantages and disadvantages and what the future development on the IPD method is; therefore, this article makes a deep and comprehensive overview for two major types of the IPD methods, the handcrafted and machine learning, including their advantages, disadvantages, localization accuracy, noise immunity and others, for over 40 years. A comprehensive analysis using average repeatability (AR) and localization error (LE) is also evaluated. The future development for the IPD is finally given.