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Computer Vision and Sensors-Based Application for Intelligent Systems
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Computer Vision and Sensors-Based Application for Intelligent Systems

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".

Deadline for manuscript submissions: 25 November 2025 | Viewed by 3999

Special Issue Editors

Prof. Dr. Jungpil Shin

E-Mail Website
Guest Editor
Pattern Processing Lab, School of Computer Science and Engineering, The University of Aizu, Aizu-Wakamatsu 965-8580, Fukushima, Japan
Interests: pattern recognition; character recognition; image processing; computer vision; human–computer interaction; neurological disease analysis; machine learning
Special Issues, Collections and Topics in MDPI journals
Dr. Yutaka Watanobe

E-Mail Website
Guest Editor
School of Computer Science and Engineering, The University of Aizu, Aizu-Wakamatsu, Fukushima 965-8580, Japan
Interests: intelligent software; smart learning; cloud robotics; programming environment; visual languages
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Modern society faces a variety of challenges, including enhancing safety, improving quality of life, eliminating disparities, upgrading service quality, boosting productivity, and strengthening disaster response. To address these challenges, the rapid evolution of intelligent systems is essential. In particular, the development of sensing technology, as represented in the field of robotics, is expected to advance, and computer vision will play a central role in this development. Therefore, this Special Issue focuses on advanced computer vision technologies that support intelligent systems and their integration. Topics related to, but not limited to, ML, DL, and HCI are welcome. In addition, this Special Issue explores solutions to specific real-world problems and their applications. Our aim is to provide useful insights for researchers and engineers and to contribute to solving real-world problems.

Prof. Dr. Jungpil Shin
Dr. Yutaka Watanobe
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. Sensors 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 2600 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

  • sensing technology
  • computer vision
  • human–computer interaction
  • image processing
  • pattern recognition
  • object detection
  • 3D reconstruction
  • virtual reality
  • machine learning
  • deep learning

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Published Papers (4 papers)

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Research

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22 pages, 6119 KiB  
Article
Development of a Software and Hardware Complex for Monitoring Processes in Production Systems
by Vadim Pechenin, Rustam Paringer, Nikolay Ruzanov and Aleksandr Khaimovich
Sensors 2025, 25(5), 1527; https://doi.org/10.3390/s25051527 - 28 Feb 2025
Viewed by 557
Abstract
The article presents a detailed exposition of a hardware–software complex that has been developed for the purpose of enhancing the productivity of accounting for the state of the production process. This complex facilitates the automation of the identification of parts in production containers [...] Read more.
The article presents a detailed exposition of a hardware–software complex that has been developed for the purpose of enhancing the productivity of accounting for the state of the production process. This complex facilitates the automation of the identification of parts in production containers and the utilisation of supplementary markers. The complex comprises a mini computer (system unit in industrial version) with connected cameras (IP or WEB), a communication module with LED and signal lamps, and developed software. The cascade algorithm developed for the detection of labels and objects in containers employs trained convolutional neural networks (YOLO and VGG19), thereby enhancing the recognition accuracy while concurrently reducing the size of the training sample for neural networks. The efficacy of the developed system was assessed through laboratory experimentation, which yielded experimental results demonstrating 93% accuracy in detail detection using the developed algorithm, in comparison to the 72% accuracy achieved through the utilisation of the traditional approach employing a single neural network. Full article
(This article belongs to the Special Issue Computer Vision and Sensors-Based Application for Intelligent Systems)
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24 pages, 6353 KiB  
Article
A Performance Study of Mobility Speed Effects on Vehicle Following Control via V2V MIMO Communications
by Jerawat Sopajarn, Apidet Booranawong, Surachate Chumpol, Nattha Jindapetch, Okuyama Yuichi and Hiroshi Saito
Sensors 2025, 25(4), 1193; https://doi.org/10.3390/s25041193 - 15 Feb 2025
Viewed by 433
Abstract
Vehicle-to-vehicle (V2V) communications are important for intelligent transportation system (ITS) development for driving safety, traffic efficiency, and the development of autonomous vehicles. V2V communication channels, environments, mobility patterns, and mobility speed significantly affect the accuracy of autonomous vehicle control. In this paper, we [...] Read more.
Vehicle-to-vehicle (V2V) communications are important for intelligent transportation system (ITS) development for driving safety, traffic efficiency, and the development of autonomous vehicles. V2V communication channels, environments, mobility patterns, and mobility speed significantly affect the accuracy of autonomous vehicle control. In this paper, we propose a versatile system-level framework that can be used for investigation, experimentation, and verification to expedite the development of autonomous vehicles. Once vehicle functionality, communication channels, and driving scenarios were modelled, experiments with different mobility speeds and communication channels were set up to measure the communication quality and the effects on the vehicle’s following control. In our experiment, the leader vehicle was set to travel through a high-building environment with a constant speed of 36 km/h and suddenly changed lanes in front of the follower vehicle. The speed of the follower vehicle ranged from 40 km/h to 80 km/h. The experimental results show that the quality of single-input and single-output (SISO) communication is less efficient than multiple-input and multiple-output (MIMO) communication. The quality of SISO communication between vehicles with a speed difference of 4 km/h (leader 36 km/h and follower 40 km/h) had a link quality worse than 0.85, which caused unstable control in the follower vehicle speed. However, it was also found that if the speed of the follower vehicle increased to 80 km/h, the link quality of SISO communication was better, close to 0.95, due to the decreased distance between the vehicles, resulting in better control. Moreover, it was found that the impact of SISO communication can be overcome by using the MIMO communication technique and selecting the best input signal at each time. MIMO communication has less signal loss, allowing the follower vehicle to make correct decisions throughout the movement. Full article
(This article belongs to the Special Issue Computer Vision and Sensors-Based Application for Intelligent Systems)
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20 pages, 1946 KiB  
Article
Two-Stream Modality-Based Deep Learning Approach for Enhanced Two-Person Human Interaction Recognition in Videos
by Hemel Sharker Akash, Md Abdur Rahim, Abu Saleh Musa Miah, Hyoun-Sup Lee, Si-Woong Jang and Jungpil Shin
Sensors 2024, 24(21), 7077; https://doi.org/10.3390/s24217077 - 3 Nov 2024
Viewed by 1673
Abstract
Human interaction recognition (HIR) between two people in videos is a critical field in computer vision and pattern recognition, aimed at identifying and understanding human interaction and actions for applications such as healthcare, surveillance, and human–computer interaction. Despite its significance, video-based HIR faces [...] Read more.
Human interaction recognition (HIR) between two people in videos is a critical field in computer vision and pattern recognition, aimed at identifying and understanding human interaction and actions for applications such as healthcare, surveillance, and human–computer interaction. Despite its significance, video-based HIR faces challenges in achieving satisfactory performance due to the complexity of human actions, variations in motion, different viewpoints, and environmental factors. In the study, we proposed a two-stream deep learning-based HIR system to address these challenges and improve the accuracy and reliability of HIR systems. In the process, two streams extract hierarchical features based on the skeleton and RGB information, respectively. In the first stream, we utilised YOLOv8-Pose for human pose extraction, then extracted features with three stacked LSM modules and enhanced them with a dense layer that is considered the final feature of the first stream. In the second stream, we utilised SAM on the input videos, and after filtering the Segment Anything Model (SAM) feature, we employed integrated LSTM and GRU to extract the long-range dependency feature and then enhanced them with a dense layer that was considered the final feature for the second stream module. Here, SAM was utilised for segmented mesh generation, and ImageNet was used for feature extraction from images or meshes, focusing on extracting relevant features from sequential image data. Moreover, we newly created a custom filter function to enhance computational efficiency and eliminate irrelevant keypoints and mesh components from the dataset. We concatenated the two stream features and produced the final feature that fed into the classification module. The extensive experiment with the two benchmark datasets of the proposed model achieved 96.56% and 96.16% accuracy, respectively. The high-performance accuracy of the proposed model proved its superiority. Full article
(This article belongs to the Special Issue Computer Vision and Sensors-Based Application for Intelligent Systems)
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Review

Jump to: Research

40 pages, 10575 KiB  
Review
A Survey of the State of the Art in Monocular 3D Human Pose Estimation: Methods, Benchmarks, and Challenges
by Yan Guo, Tianhan Gao, Aoshuang Dong, Xinbei Jiang, Zichen Zhu and Fuxin Wang
Sensors 2025, 25(8), 2409; https://doi.org/10.3390/s25082409 - 10 Apr 2025
Viewed by 378
Abstract
Three-dimensional human pose estimation (3D HPE) from monocular RGB cameras is a fundamental yet challenging task in computer vision, forming the basis of a wide range of applications such as action recognition, metaverse, self-driving, and healthcare. Recent advances in deep learning have significantly [...] Read more.
Three-dimensional human pose estimation (3D HPE) from monocular RGB cameras is a fundamental yet challenging task in computer vision, forming the basis of a wide range of applications such as action recognition, metaverse, self-driving, and healthcare. Recent advances in deep learning have significantly propelled the field, particularly with the incorporation of state-space models (SSMs) and diffusion models. However, systematic reviews that comprehensively cover these emerging techniques remain limited. This survey contributes to the literature by providing the first comprehensive analysis of recent innovative approaches, featuring diffusion models and SSMs within 3D HPE. It categorizes and analyzes various techniques, highlighting their strengths, limitations, and notable innovations. Additionally, it provides a detailed overview of commonly employed datasets and evaluation metrics. Furthermore, this survey offers an in-depth discussion on key challenges, particularly depth ambiguity and occlusion issues arising from single-view setups, thoroughly reviewing effective solutions proposed in recent studies. Finally, current applications and promising avenues for future research are highlighted to guide and inspire ongoing innovation in the area, with emerging trends such as integrating large language models (LLMs) to provide semantic priors and prompt-based supervision for improved 3D pose estimation. Full article
(This article belongs to the Special Issue Computer Vision and Sensors-Based Application for Intelligent Systems)
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