Robust feature extraction algorithm suitable for real-time embedded applications

• Published in: Journal of real-time image processing 2018-03, Vol.14 (3), p.647-665
• Main Authors: Aguilar-González, Abiel, Arias-Estrada, Miguel, Berry, François
• Format: Article
• Language: English
• Subjects: Accuracy; Algorithms; Calibration; Cameras; Computer Graphics; Computer Science; Computer Vision and Pattern Recognition; Computers; Embedded systems; Feature extraction; Field programmable gate arrays; Hardware Architecture; Image Processing and Computer Vision; Localization; Multimedia Information Systems; Pattern Recognition; Pixels; Real time; Robotics; Robustness; Signal,Image and Speech Processing; Special Issue Paper; Surveillance; Systems design; Vision systems
• ISSN: 1861-8200; 1861-8219
• DOI: 10.1007/s11554-017-0701-8

Smart cameras integrate processing close to the image sensor, so they can deliver high-level information to a host computer or high-level decision process. One of the most common processing is the visual features extraction since many vision-based use-cases are based on such algorithm. Unfortunately, in most of cases, features detection algorithms are not robust or do not reach real-time processing. Based on these limitations, a feature detection algorithm that is robust enough to deliver robust features under any type of indoor/outdoor scenarios is proposed. This was achieved by applying a non-textured corner filter combined to a subpixel refinement. Furthermore, an FPGA architecture is proposed. This architecture allows compact system design, real-time processing for Full HD images (it can process up to 44 frames/91.238.400 pixels per second for Full HD images), and high efficiency for smart camera implementations (similar hardware resources than previous formulations without subpixel refinement and without non-textured corner filter). For accuracy/robustness, experimental results for several real-world scenes are encouraging and show the feasibility of our algorithmic approach.