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Pattern Recognition in Video Processing

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 10 July 2025 | Viewed by 856

Special Issue Editors


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Guest Editor
School of Computer Science, Chongqing University, Chongqing 400044, China
Interests: video processing; machine learning

E-Mail Website
Guest Editor
School of Computer Science, Chongqing University, Chongqing 400044, China
Interests: video processing; machine learning

E-Mail Website
Guest Editor
1. College of Mechanical and Vehicle Engineering, Chongqing University, Chongqing 400044, China
2. The State Key Laboratory of Mechanical Transmission for Advanced Equipment, Chongqing University, Chongqing 400044, China
Interests: signal processing; fault feature extraction; fault prognosis; life prediction; fault transfer diagnosis; vision measurement; digital twin; energy harvesting for sensors
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Video processing has become increasingly important due to the proliferation of video content in various domains, such as surveillance, entertainment, healthcare, and robotics. Pattern recognition techniques are crucial in extracting meaningful information from video data, enabling a wide range of applications. This Special Issue aims to showcase the latest advancements, research findings, and innovative solutions in pattern recognition for video processing.

We invite original research articles, review papers, and case studies that explore the intersection of pattern recognition and video processing. Topics of interest include, but are not limited to, the following:

  • Object detection, tracking, and recognition in video;
  • Action and activity recognition in video sequences;
  • Facial expression analysis and emotion recognition in video;
  • Gesture recognition and human–computer interaction in video;
  • Video-based biometrics and person identification;
  • Video anomaly detection and event recognition;
  • Video segmentation and scene understanding;
  • Video-based crowd analysis and behavior understanding;
  • Deep learning techniques for video pattern recognition;
  • Transfer learning and domain adaptation for video analysis;
  • Multimodal fusion for video pattern recognition;
  • Efficient algorithms and architectures for real-time video processing;
  • Applications of pattern recognition in video surveillance, autonomous vehicles, sports analysis, and more.

We encourage submissions that present novel methodologies, innovative applications, and significant improvements over existing techniques, demonstrating strong theoretical foundations, comprehensive empirical evaluations, and practical impacts in real-world scenarios.

Dr. Mingliang Zhou
Dr. Xuekai Wei
Prof. Dr. Yi Qin
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. Applied Sciences 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

  • video pattern recognition
  • object detection and tracking
  • action and activity recognition
  • facial expression analysis
  • gesture recognition
  • video anomaly detection
  • deep learning for video analysis
  • real-time video processing
  • video surveillance applications
  • autonomous vehicle perception

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Published Papers (1 paper)

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Research

21 pages, 732 KiB  
Article
Efficient Access Control for Video Anomaly Detection Using ABE-Based User-Level Revocation with Ciphertext and Index Updates
by Lu Jiang, Jielu Yan, Weizhi Xian, Xuekai Wei and Xiaofeng Liao
Appl. Sci. 2025, 15(9), 5128; https://doi.org/10.3390/app15095128 - 5 May 2025
Viewed by 225
Abstract
With the widespread deployment of video surveillance systems, effective access control is essential to enhance the accuracy and security of video anomaly detection. This paper proposes a Searchable and Revocable Attribute-Based Encryption scheme (ABE-RS) that is specifically designed for dynamic video anomaly detection [...] Read more.
With the widespread deployment of video surveillance systems, effective access control is essential to enhance the accuracy and security of video anomaly detection. This paper proposes a Searchable and Revocable Attribute-Based Encryption scheme (ABE-RS) that is specifically designed for dynamic video anomaly detection scenarios. By integrating a user management tree structure, attribute-based key distribution, and keyword grouping techniques, the proposed scheme enables efficient user-level revocation along with dynamic updates to ciphertexts and keyword indexes. Furthermore, an inverted index structure is introduced to accelerate keyword search, facilitating the rapid detection and retrieval of anomalous video events. Formal security analysis demonstrates that the scheme is secure against chosen plaintext attacks (CPAs) and chosen keyword attacks (CKAs). The experimental results demonstrate that the scheme maintains millisecond-level revocation efficiency in methodology involving 512 users and either 50 attributes or a thousand keywords. Full article
(This article belongs to the Special Issue Pattern Recognition in Video Processing)
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