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Algorithms
Special Issues
Deep Learning in Intelligent Video Surveillance
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Deep Learning in Intelligent Video Surveillance

A special issue of Algorithms (ISSN 1999-4893).

Deadline for manuscript submissions: closed (30 November 2021) | Viewed by 3102

Special Issue Editors

Dr. Guanqiu Qi

E-Mail Website
Guest Editor
Department of Computer Information Systems, State University of New York at Buffalo State, Buffalo, NY 14222, USA
Interests: computer vision; image processing; pattern recognition; machine learning
Special Issues, Collections and Topics in MDPI journals
Dr. Zhiqin Zhu

E-Mail Website
Guest Editor
College of Automation, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
Interests: image color analysis; image enhancement; image fusion; image restoration
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear colleagues,

Different from the responsibility of people in security, tracking is done by machines in intelligent video surveillance. Therefore, a computer receives either some pre-recorded videos from surveillance cameras or real-time video and attempts to differentiate people and classify them. With the help of tracking, the shapes of every person and their movements can be identified in the scene. Person re-identification is the task of associating images of the same person taken from different cameras or from the same camera in different occasions, which has been widely used in intelligent video surveillance. Deep learning techniques are conducive to improving the performance of person re-identification, which ensures an in-depth look into identifying people from different angles and locations. With increasing computation capacity, crimes and other illegal actions can be prevented and the offenders could be easily tracked in a timely manner, which can effectively maintain the safety of society.

Dr. Guanqiu Qi
Dr. Zhiqin Zhu
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. Algorithms 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 1600 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

  • person re-identification
  • deep learning
  • pattern recognition
  • intelligent monitoring

Published Papers (1 paper)

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Research

14 pages, 743 KiB  
Article
A Domain Adaptive Person Re-Identification Based on Dual Attention Mechanism and Camstyle Transfer
by Chengyan Zhong, Guanqiu Qi, Neal Mazur, Sarbani Banerjee, Devanshi Malaviya and Gang Hu
Algorithms 2021, 14(12), 361; https://0-doi-org.brum.beds.ac.uk/10.3390/a14120361 - 13 Dec 2021
Cited by 5 | Viewed by 2177
Abstract
Due to the variation in the image capturing process, the difference between source and target sets causes a challenge in unsupervised domain adaptation (UDA) on person re-identification (re-ID). Given a labeled source training set and an unlabeled target training set, this paper focuses [...] Read more.
Due to the variation in the image capturing process, the difference between source and target sets causes a challenge in unsupervised domain adaptation (UDA) on person re-identification (re-ID). Given a labeled source training set and an unlabeled target training set, this paper focuses on improving the generalization ability of the re-ID model on the target testing set. The proposed method enforces two properties at the same time: (1) camera invariance is achieved through the positive learning formed by unlabeled target images and their camera style transfer counterparts; and (2) the robustness of the backbone network feature extraction is improved, and the accuracy of feature extraction is enhanced by adding a position-channel dual attention mechanism. The proposed network model uses a classic dual-stream network. Comparative experimental results on three public benchmarks prove the superiority of the proposed method. Full article
(This article belongs to the Special Issue Deep Learning in Intelligent Video Surveillance)
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