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Image and Video Processing and Recognition Based on Artificial Intelligence: 3rd Edition

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

Deadline for manuscript submissions: 25 September 2024 | Viewed by 1049

Special Issue Editors

Special Issue Information

Dear Colleagues,

Recent developments have led to the vivid application of artificial intelligence (AI) and sensing techniques to image and video processing and recognition. While state-of-the-art technology has matured, its performance is still affected by various environmental conditions and heterogeneous databases. This Special Issue invites high-quality and state-of-the-art academic papers on challenging issues in the field of AI and sensing-based image and video processing and recognition. We solicit original papers of unpublished and completed research that are not currently under review by any other conference, magazine, or journal. Topics of interest include, but are not limited to, the following:

  • AI and sensing-technique-based image processing, understanding, recognition, compression, and reconstruction;
  • AI and sensing-technique-based video processing, understanding, recognition, compression, and reconstruction;
  • Computer vision based on AI and sensing techniques;
  • AI and sensing-technique-based biometrics;
  • AI and sensing-technique-based object detection and tracking;
  • Approaches that combine AI and sensing techniques and conventional methods for image and video processing and recognition;
  • Generative adversarial network (GAN)-based image and video processing and recognition;
  • Approaches that combine AI and blockchain methods for image and video processing and recognition.

Prof. Dr. Kang Ryoung Park
Prof. Dr. Sangyoun Lee
Prof. Dr. Euntai Kim
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

  • image processing, understanding, recognition, compression, and reconstruction based on sensing techniques and AI
  • video processing, understanding, recognition, compression, and reconstruction based on sensing techniques and AI
  • computer vision based on sensing techniques and AI
  • biometrics based on sensing techniques and AI
  • AI fusion and conventional methods
  • AI fusion and blockchain methods

Related Special Issue

Published Papers (1 paper)

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Research

15 pages, 861 KiB  
Article
Conv3D-Based Video Violence Detection Network Using Optical Flow and RGB Data
by Jae-Hyuk Park, Mohamed Mahmoud and Hyun-Soo Kang
Sensors 2024, 24(2), 317; https://doi.org/10.3390/s24020317 - 05 Jan 2024
Cited by 1 | Viewed by 717
Abstract
Detecting violent behavior in videos to ensure public safety and security poses a significant challenge. Precisely identifying and categorizing instances of violence in real-life closed-circuit television, which vary across specifications and locations, requires comprehensive understanding and processing of the sequential information embedded in [...] Read more.
Detecting violent behavior in videos to ensure public safety and security poses a significant challenge. Precisely identifying and categorizing instances of violence in real-life closed-circuit television, which vary across specifications and locations, requires comprehensive understanding and processing of the sequential information embedded in these videos. This study aims to introduce a model that adeptly grasps the spatiotemporal context of videos within diverse settings and specifications of violent scenarios. We propose a method to accurately capture spatiotemporal features linked to violent behaviors using optical flow and RGB data. The approach leverages a Conv3D-based ResNet-3D model as the foundational network, capable of handling high-dimensional video data. The efficiency and accuracy of violence detection are enhanced by integrating an attention mechanism, which assigns greater weight to the most crucial frames within the RGB and optical-flow sequences during instances of violence. Our model was evaluated on the UBI-Fight, Hockey, Crowd, and Movie-Fights datasets; the proposed method outperformed existing state-of-the-art techniques, achieving area under the curve scores of 95.4, 98.1, 94.5, and 100.0 on the respective datasets. Moreover, this research not only has the potential to be applied in real-time surveillance systems but also promises to contribute to a broader spectrum of research in video analysis and understanding. Full article
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