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Applied Sciences
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Recent Advances in Artificial Intelligence for Computer Vision
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Recent Advances in Artificial Intelligence for Computer Vision

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

Deadline for manuscript submissions: closed (30 December 2023) | Viewed by 5006

Special Issue Editor

Prof. Dr. Zulfiqar Habib

E-Mail Website
Guest Editor
Department of Computer Science, COMSATS University Islamabad (CUI), Lahore Campus, Lahore, Pakistan
Interests: computer vision; digital image processing; computer graphics; robotics; machine learning

Special Issue Information

Dear Colleagues,

Computer vision (CV) and artificial intelligence (AI) are rapidly progressing areas in the applied sciences and been proven essential in science, engineering, bio, and even art programs. These technologies have great potential to integrate reliability, automation, and value addition in our daily lives and industry. Beyond the conventional machine learning techniques, state-of-the-art AI algorithms based on convolutional neural networks such as deep learning, transformer model, extreme learning, transfer learning, generative adversarial networks (GANs), and reinforcement learning are now being effectively used for CV applications. Considering the above, we are pleased to open a new Special Issue entitled “Recent Advances in Artificial Intelligence for Computer Vision”.

Prof. Dr. Zulfiqar Habib
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. 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

  • computer vision
  • digital image processing
  • artificial intelligence
  • machine learning
  • deep learning
  • reinforcement learning
  • video analytics
  • image/video forensic analysis
  • super resolution
  • medical imaging

Published Papers (2 papers)

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Research

21 pages, 2852 KiB  
Article
Ensemble-Based Knowledge Distillation for Video Anomaly Detection
by Burçak Asal and Ahmet Burak Can
Appl. Sci. 2024, 14(3), 1032; https://doi.org/10.3390/app14031032 - 25 Jan 2024
Viewed by 749
Abstract
Video anomaly detection has become a vital task for smart video surveillance systems because of its significant potential to minimize the video data to be analyzed by choosing unusual and critical patterns in the scenes. In this paper, we introduce three novel ensemble [...] Read more.
Video anomaly detection has become a vital task for smart video surveillance systems because of its significant potential to minimize the video data to be analyzed by choosing unusual and critical patterns in the scenes. In this paper, we introduce three novel ensemble and knowledge distillation-based adaptive training methods to handle robust detection of different abnormal patterns in video scenes. Our approach leverages the adaptation process by providing information transfer from multiple teacher models with different network structures and further alleviates the catastrophic forgetting issue. The proposed ensemble knowledge distillation methods are implemented on two state-of-the-art anomaly detection models. We extensively evaluate our methods on two public video anomaly datasets and present a detailed analysis of our results. Finally, we show that not only does our best version model achieve comparable performance with a frame-level AUC of 75.82 to other state-of-the-art models on UCF-Crime as the target dataset, but more importantly our approaches prevent catastrophic forgetting and dramatically improve our model’s performance. Full article
(This article belongs to the Special Issue Recent Advances in Artificial Intelligence for Computer Vision)
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15 pages, 6105 KiB  
Article
Shoplifting Detection Using Hybrid Neural Network CNN-BiLSMT and Development of Benchmark Dataset
by Iqra Muneer, Mubbashar Saddique, Zulfiqar Habib and Heba G. Mohamed
Appl. Sci. 2023, 13(14), 8341; https://doi.org/10.3390/app13148341 - 19 Jul 2023
Viewed by 3510
Abstract
Shoplifting poses a significant challenge for shop owners as well as other stakeholders, including law enforcement agencies. In recent years, the task of shoplifting detection has gained the interest of researchers due to video surveillance generating vast quantities of data that cannot be [...] Read more.
Shoplifting poses a significant challenge for shop owners as well as other stakeholders, including law enforcement agencies. In recent years, the task of shoplifting detection has gained the interest of researchers due to video surveillance generating vast quantities of data that cannot be processed in real-time by human staff. In previous studies, different datasets and methods have been developed for the task of shoplifting detection. However, there is a lack of a large benchmark dataset containing different behaviors of shoplifting and standard methods for the task of shoplifting detection. To overcome this limitation, in this study, a large benchmark dataset has been developed, having 900 instances with 450 cases of shoplifting and 450 of non-shoplifting with manual annotation based on five different ways of shoplifting. Moreover, a method for the detection of shoplifting is proposed for evaluating the developed dataset. The dataset is also evaluated with methods as baseline methods, including 2D CNN and 3D CNN. Our proposed method, which is a combination of Inception V3 and BILSTM, outperforms all baseline methods with 81 % accuracy. The developed dataset will be publicly available to foster in various areas related to human activity recognition. These areas encompass the development of systems for detecting behaviors such as robbery, identifying human movements, enhancing safety measures, and detecting instances of theft. Full article
(This article belongs to the Special Issue Recent Advances in Artificial Intelligence for Computer Vision)
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