{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T19:55:24Z","timestamp":1790106924396,"version":"4.0.1"},"reference-count":39,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2025,5,28]],"date-time":"2025-05-28T00:00:00Z","timestamp":1748390400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>This paper presents a real-time system for feature detection and description, the first stage in a structure-from-motion (SfM) pipeline. The proposed system leverages an optimized version of the ORB algorithm (oriented FAST and rotated BRIEF) implemented on the Digilent Zybo Z7020 FPGA board equipped with the Xilinx Zynq-7000 SoC. The system accepts real-time video input (60 fps, 1920 \u00d7 1080 resolution, 24-bit color) via HDMI or a camera module. In order to support high frame rates for full-HD images, a double-data-rate pipeline scheme was adopted for Harris functions. Gray-scale video with features identified in red is exported through a separate HDMI port. Feature descriptors are calculated inside the FPGA by Zynq\u2019s programmable logic and verified using Xilinx\u2019s ILA IP block on a connected computer running Vivado. The implemented system achieves a latency of 192.7 microseconds, which is suitable for real-time applications. The proposed architecture is evaluated in terms of repeatability, matching retention and matching accuracy in several image transformations. It meets satisfactory accuracy and performance considering that there are slight changes between successive frames. This work paves the way for future research on the implementation of the remaining stages of a real-time SfM pipeline on the proposed hardware platform.<\/jats:p>","DOI":"10.3390\/jimaging11060178","type":"journal-article","created":{"date-parts":[[2025,5,28]],"date-time":"2025-05-28T06:30:26Z","timestamp":1748413826000},"page":"178","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["High-Throughput ORB Feature Extraction on Zynq SoC for Real-Time Structure-from-Motion Pipelines"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-1568-8137","authenticated-orcid":false,"given":"Panteleimon","family":"Stamatakis","sequence":"first","affiliation":[{"name":"Department of Computer, Informatics & Telecommunications Engineering, International Hellenic University, Terma Magnisias, 62121 Serres, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3709-6395","authenticated-orcid":false,"given":"John","family":"Vourvoulakis","sequence":"additional","affiliation":[{"name":"Department of Computer, Informatics & Telecommunications Engineering, International Hellenic University, Terma Magnisias, 62121 Serres, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,5,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"730","DOI":"10.1109\/TPAMI.2015.2469274","article-title":"High Accuracy Monocular SFM and Scale Correction for Autonomous Driving","volume":"38","author":"Song","year":"2016","journal-title":"IEEE Trans. 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