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Current Challenges and Techniques: Computer Vision, Deep Learning, and Machine...
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Current Challenges and Techniques: Computer Vision, Deep Learning, and Machine Learning for Crime Prevention in Smart Cities

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: 16 November 2024 | Viewed by 2806

Special Issue Editors

Dr. Fath U Min Ullah

E-Mail Website
Guest Editor
School of Engineering and Computing, University of Central Lancashire, Preston PR1 2HE, UK
Interests: computer vision; image processing; artificial intelligence; pattern recognition; machine learning
Special Issues, Collections and Topics in MDPI journals
Dr. Estefanía Talavera

E-Mail Website
Guest Editor
Data Management and Biometrics group, University of Twente, 7500 AE Enschede, The Netherlands
Interests: computer vision; human behaviour understanding; video analysis; multimodal learning
Prof. Dr. Nuno Gonçalves

E-Mail Website
Guest Editor
Institute of Systems and Robotics, University of Coimbra, 3000-456 Coimbra, Portugal
Interests: computer vision; biometrics; machine learning and computer graphics

Special Issue Information

Dear Colleagues,

Human across the globe lives have become more comfortable as a result of advancements in technology, used in adapting machine intelligence and deep learning-based techniques, together with the increased number of installed surveillance cameras. The purpose of these cameras is to monitor human activities and enable object detection, video recognition, protection of human assets, and identifying the state of certain actions via CCTV footage to prevent crimes and the occurrence of avoid abnormal events. However, along with these cameras, the involvement of humans in camera-based monitoring has also risen and is becoming increasingly costly and problematic to intelligently manage. An automatic system for such monitoring of activities will ease the detection and recognition of ongoing events. The main objective of detecting these events is to reduce crime rates and create a more secure and safe environment.

Topics of interest include but are not limited to:

  • Computer vision in forensics
  • Biometrics for security
  • Monitoring of activity, interaction and/or intention from videos
  • Egocentric vision for surveillance
  • Detection, tracking and recognition
  • Activity recognition
  • Analysis of abnormal activities
  • AI-assisted technologies for security
  • Violence detection 

Dr. Fath U Min Ullah
Dr. Estefanía Talavera
Prof. Dr. Nuno Gonçalves
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
  • image processing
  • deep learning
  • machine learning
  • crime prevention
  • surveillance videos

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Published Papers (2 papers)

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Research

11 pages, 935 KiB  
Article
Next-Gen Dynamic Hand Gesture Recognition: MediaPipe, Inception-v3 and LSTM-Based Enhanced Deep Learning Model
by Yaseen, Oh-Jin Kwon, Jaeho Kim, Sonain Jamil, Jinhee Lee and Faiz Ullah
Electronics 2024, 13(16), 3233; https://doi.org/10.3390/electronics13163233 - 15 Aug 2024
Viewed by 707
Abstract
Gesture recognition is crucial in computer vision-based applications, such as drone control, gaming, virtual and augmented reality (VR/AR), and security, especially in human–computer interaction (HCI)-based systems. There are two types of gesture recognition systems, i.e., static and dynamic. However, our focus in this [...] Read more.
Gesture recognition is crucial in computer vision-based applications, such as drone control, gaming, virtual and augmented reality (VR/AR), and security, especially in human–computer interaction (HCI)-based systems. There are two types of gesture recognition systems, i.e., static and dynamic. However, our focus in this paper is on dynamic gesture recognition. In dynamic hand gesture recognition systems, the sequences of frames, i.e., temporal data, pose significant processing challenges and reduce efficiency compared to static gestures. These data become multi-dimensional compared to static images because spatial and temporal data are being processed, which demands complex deep learning (DL) models with increased computational costs. This article presents a novel triple-layer algorithm that efficiently reduces the 3D feature map into 1D row vectors and enhances the overall performance. First, we process the individual images in a given sequence using the MediaPipe framework and extract the regions of interest (ROI). The processed cropped image is then passed to the Inception-v3 for the 2D feature extractor. Finally, a long short-term memory (LSTM) network is used as a temporal feature extractor and classifier. Our proposed method achieves an average accuracy of more than 89.7%. The experimental results also show that the proposed framework outperforms existing state-of-the-art methods. Full article
(This article belongs to the Special Issue Current Challenges and Techniques: Computer Vision, Deep Learning, and Machine Learning for Crime Prevention in Smart Cities)
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10 pages, 3891 KiB  
Article
Improved Vehicle Detection Using Weather Classification and Faster R-CNN with Dark Channel Prior
by Ershang Tian and Juntae Kim
Electronics 2023, 12(14), 3022; https://doi.org/10.3390/electronics12143022 - 10 Jul 2023
Cited by 5 | Viewed by 1441
Abstract
Recent advancements in artificial intelligence have led to significant improvements in object detection. Researchers have focused on enhancing the performance of object detection in challenging environments, as this has the potential to enhance practical applications. Deep learning has been successful in image classification [...] Read more.
Recent advancements in artificial intelligence have led to significant improvements in object detection. Researchers have focused on enhancing the performance of object detection in challenging environments, as this has the potential to enhance practical applications. Deep learning has been successful in image classification and target detection and has a wide range of applications, including vehicle detection. However, object detection models trained on high-quality images often struggle to perform well under adverse weather conditions, such as fog and rain. In this paper, we propose an improved vehicle detection method using weather classification and a Faster R-CNN with a dark channel prior (DCP). The proposed method first classifies the weather within the image, preprocesses the image using the dark channel prior (DCP) based on the classification result, and then performs vehicle detection on the preprocessed image using a Faster R-CNN. The effectiveness of the proposed method is shown through experiments with images in various weather conditions. Full article
(This article belongs to the Special Issue Current Challenges and Techniques: Computer Vision, Deep Learning, and Machine Learning for Crime Prevention in Smart Cities)
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