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Electronics
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Innovation and Technology of Computer Vision
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Innovation and Technology of Computer Vision

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: 15 July 2025 | Viewed by 997

Special Issue Editors

Dr. Shuyan Li

E-Mail Website
Guest Editor
School of Electronics, Electrical Engineering and Computer Science, Queen's University Belfast, Belfast BT9 7AE, UK
Interests: deep learning; computer vision
Dr. Zhao Huang

E-Mail Website
Guest Editor
Department of Computer and Information Science, Northumbria University, Newcastle NE1 8ST, UK
Interests: robotics and human localization; intelligence sensing; human behaviour recognition; IoT; sensors

Special Issue Information

Dear Colleagues,

This Special Issue on Innovation and Technology of Computer Vision in Electronics explores cutting-edge advancements and applications of computer vision in digital twin systems. Digital twins serve as virtual representations of physical systems, enabling real-time data integration, analysis, and decision-making across various industries. Computer vision plays a crucial role in enhancing the functionality of digital twins by enabling precise 3D modeling, defect detection, and real-time updates. This Special Issue welcomes original research and review articles focusing on innovative methodologies, frameworks, and applications that integrate computer vision into digital twin systems. Topics of interest include real-time data fusion, AI-driven visual analytics, and advancements in image processing and simulation techniques. By addressing emerging challenges in accuracy, scalability, and implementation, this Special Issue aims to push the boundaries of how computer vision can contribute to the evolution of digital twin technologies.

Dr. Shuyan Li
Dr. Zhao Huang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • computer vision
  • digital twin
  • smart systems
  • AI-driven
  • visual analytics

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Published Papers (1 paper)

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Research

22 pages, 2212 KiB  
Article
KeypointNet: An Efficient Deep Learning Model with Multi-View Recognition Capability for Sitting Posture Recognition
by Zheng Cao, Xuan Wu, Chunguo Wu, Shuyang Jiao, Yubin Xiao, Yu Zhang and You Zhou
Electronics 2025, 14(4), 718; https://doi.org/10.3390/electronics14040718 - 12 Feb 2025
Viewed by 691
Abstract
Numerous studies leverage pose estimation to extract human keypoint data and then classify sitting postures. However, employing neural networks for direct keypoint classification often yields suboptimal results. Alternatively, modeling keypoints into other data representations before classification introduces redundant information and substantially increases inference [...] Read more.
Numerous studies leverage pose estimation to extract human keypoint data and then classify sitting postures. However, employing neural networks for direct keypoint classification often yields suboptimal results. Alternatively, modeling keypoints into other data representations before classification introduces redundant information and substantially increases inference time. In addition, most existing methods perform well only under a single fixed viewpoint, limiting their applicability in complex real-world scenarios involving unseen viewpoints. To better address the first limitation, we propose KeypointNet, which employs a decoupled feature extraction strategy consisting of a Keypoint Feature Extraction module and a Multi-Scale Feature Extraction module. In addition, to enhance multi-view recognition capability, we propose the Multi-View Simulation (MVS) algorithm, which augments the viewpoint information by first rotating keypoints and then repositioning the camera. Simultaneously, we propose the multi-view sitting posture (MVSP) dataset, designed to simulate diverse real-world viewpoints. The experimental results demonstrate that KeypointNet outperforms the other state-of-the-art methods on both the proposed MVSP dataset and the other public datasets, while maintaining a lightweight and efficient design. Ablation studies demonstrate the effectiveness of MVS and all KeypointNet modules. Furthermore, additional experiments highlight the superior generalization, small-sample learning capability, and robustness to unseen viewpoints of KeypointNet. Full article
(This article belongs to the Special Issue Innovation and Technology of Computer Vision)
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