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J. Imaging
Special Issues
Advancing Action Recognition: Novel Approaches, Techniques and Applications
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Advancing Action Recognition: Novel Approaches, Techniques and Applications

A special issue of Journal of Imaging (ISSN 2313-433X).

Deadline for manuscript submissions: 31 July 2025 | Viewed by 2601

Special Issue Editor

Dr. Xiaoguang Zhu

E-Mail Website
Guest Editor
DataLab: Data Science and Informatics, University of California, Davis, CA 95616, USA
Interests: AI for health; computer vision; human action recognition; image retrieval; 3D reconstruction; EEG signal classification

Special Issue Information

Dear Colleagues,

"Advancing Action Recognition: Novel Approaches, Techniques and Applications" aims to explore cutting-edge research in the field of action recognition, focusing on innovative approaches, techniques, and their practical applications. Action recognition, a fundamental task in computer vision and artificial intelligence, holds immense significance across various domains, from surveillance and security to healthcare, robotics, and human–computer interaction. In this era of rapid technological advancement, the quest for innovative methodologies, techniques, and applications in action recognition has never been more critical.

This Special Issue seeks to showcase recent advancements in machine learning, computer vision, and related areas, with a particular emphasis on novel methodologies for detecting and understanding human actions in various contexts. Topics of interest include but are not limited to deep learning models, spatiotemporal modeling techniques, multimodal fusion techniques, transfer learning methods, and model generalization methods. Moreover, the action recognition techniques enhanced by large language models are also a heating topic for discussion.

Furthermore, the Special Issue seeks to highlight emerging applications and novel contexts where action recognition technology can make a transformative impact. Whether it is enhancing security measures through intelligent surveillance systems, revolutionizing healthcare with gesture-based interfaces and patient monitoring solutions, or empowering robots with the ability to understand and interact with human actions, the potential applications of action recognition are vast and far-reaching. By bringing together researchers and practitioners, this Special Issue aims to foster collaboration and drive forward the state of the art in action recognition technology.

Dr. Xiaoguang Zhu
Guest Editor

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. Journal of Imaging is an international peer-reviewed open access monthly 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 1800 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

  • action recognition
  • multi-modal fusion
  • anomaly detection
  • domain adaptation/generalization
  • computer vision
  • video assessment
  • robotics
  • video understanding

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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23 pages, 10925 KiB  
Article
Supervised and Self-Supervised Learning for Assembly Line Action Recognition
by Christopher Indris, Fady Ibrahim, Hatem Ibrahem, Götz Bramesfeld, Jie Huo, Hafiz Mughees Ahmad, Syed Khizer Hayat and Guanghui Wang
J. Imaging 2025, 11(1), 17; https://doi.org/10.3390/jimaging11010017 - 10 Jan 2025
Viewed by 1139
Abstract
The safety and efficiency of assembly lines are critical to manufacturing, but human supervisors cannot oversee all activities simultaneously. This study addresses this challenge by performing a comparative study to construct an initial real-time, semi-supervised temporal action recognition setup for monitoring worker actions [...] Read more.
The safety and efficiency of assembly lines are critical to manufacturing, but human supervisors cannot oversee all activities simultaneously. This study addresses this challenge by performing a comparative study to construct an initial real-time, semi-supervised temporal action recognition setup for monitoring worker actions on assembly lines. Various feature extractors and localization models were benchmarked using a new assembly dataset, with the I3D model achieving an average mAP@IoU=0.1:0.7 of 85% without optical flow or fine-tuning. The comparative study was extended to self-supervised learning via a modified SPOT model, which achieved a mAP@IoU=0.1:0.7 of 65% with just 10% of the data labeled using extractor architectures from the fully-supervised portion. Milestones include high scores for both fully and semi-supervised learning on this dataset and improved SPOT performance on ANet1.3. This study identified the particularities of the problem, which were leveraged and referenced to explain the results observed in semi-supervised scenarios. The findings highlight the potential for developing a scalable solution in the future, providing labour efficiency and safety compliance for manufacturers. Full article
(This article belongs to the Special Issue Advancing Action Recognition: Novel Approaches, Techniques and Applications)
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Review

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57 pages, 8107 KiB  
Review
Machine Learning for Human Activity Recognition: State-of-the-Art Techniques and Emerging Trends
by Md Amran Hossen and Pg Emeroylariffion Abas
J. Imaging 2025, 11(3), 91; https://doi.org/10.3390/jimaging11030091 - 20 Mar 2025
Cited by 1 | Viewed by 945
Abstract
Human activity recognition (HAR) has emerged as a transformative field with widespread applications, leveraging diverse sensor modalities to accurately identify and classify human activities. This paper provides a comprehensive review of HAR techniques, focusing on the integration of sensor-based, vision-based, and hybrid methodologies. [...] Read more.
Human activity recognition (HAR) has emerged as a transformative field with widespread applications, leveraging diverse sensor modalities to accurately identify and classify human activities. This paper provides a comprehensive review of HAR techniques, focusing on the integration of sensor-based, vision-based, and hybrid methodologies. It explores the strengths and limitations of commonly used modalities, such as RGB images/videos, depth sensors, motion capture systems, wearable devices, and emerging technologies like radar and Wi-Fi channel state information. The review also discusses traditional machine learning approaches, including supervised and unsupervised learning, alongside cutting-edge advancements in deep learning, such as convolutional and recurrent neural networks, attention mechanisms, and reinforcement learning frameworks. Despite significant progress, HAR still faces critical challenges, including handling environmental variability, ensuring model interpretability, and achieving high recognition accuracy in complex, real-world scenarios. Future research directions emphasise the need for improved multimodal sensor fusion, adaptive and personalised models, and the integration of edge computing for real-time analysis. Additionally, addressing ethical considerations, such as privacy and algorithmic fairness, remains a priority as HAR systems become more pervasive. This study highlights the evolving landscape of HAR and outlines strategies for future advancements that can enhance the reliability and applicability of HAR technologies in diverse domains. Full article
(This article belongs to the Special Issue Advancing Action Recognition: Novel Approaches, Techniques and Applications)
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