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Electronics
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Intelligent Video Analytics
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Intelligent Video Analytics

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

Deadline for manuscript submissions: closed (30 June 2021) | Viewed by 4326

Special Issue Editor

Dr. Jun Liu

E-Mail Website
Guest Editor
Information Systems Technology and Design (ISTD) Pillar, Singapore University of Technology and Design, Singapore 487372, Singapore
Interests: artificial intelligence; computer vision; machine learning

Special Issue Information

Dear Colleagues,

Intelligent video analytics is a hot and important research area thanks to its relevance to a wide range of applications, including security, robotics, autonomous vehicles, human–machine interaction, etc. This Special Issue aims to encourage research in different aspects of intelligent video analytics. This area is largely open due to the difficulties in modeling multidimensional data, the challenges in extracting informative information, and also the computational overhead and lack of large and representative datasets in many application scenarios. 

The topics of this Special Issue include but are not limited to:

  • Action recognition;
  • Image/video segmentation;
  • Object detection and tracking;
  • Video synthesis;
  • Self-supervised video understanding;
  • Sequential data (e.g., skeleton sequence) modeling;
  • Other topics related to intelligent video analytics.

Dr. Jun Liu
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. 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.

Published Papers (2 papers)

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Research

21 pages, 3326 KiB  
Article
Adaptive Method for Modeling of Temporal Dependencies between Fields of Vision in Multi-Camera Surveillance Systems
by Karol Lisowski and Andrzej Czyżewski
Electronics 2021, 10(11), 1303; https://doi.org/10.3390/electronics10111303 - 30 May 2021
Cited by 1 | Viewed by 1731
Abstract
A method of modeling the time of object transition between given pairs of cameras based on the Gaussian Mixture Model (GMM) is proposed in this article. Temporal dependencies modeling is a part of object re-identification based on the multi-camera experimental framework. The previously [...] Read more.
A method of modeling the time of object transition between given pairs of cameras based on the Gaussian Mixture Model (GMM) is proposed in this article. Temporal dependencies modeling is a part of object re-identification based on the multi-camera experimental framework. The previously utilized Expectation-Maximization (EM) approach, requiring setting the number of mixtures arbitrarily as an input parameter, was extended with the algorithm that automatically adapts the model to statistical data. The probabilistic model was obtained by matching to the histogram of transition times between a particular pair of cameras. The proposed matching procedure uses a modified particle swarm optimization (mPSO). A way of using models of transition time in object re-identification is also presented. Experiments with the proposed method of modeling the transition time were carried out, and a comparison between previous and novel approach results are also presented, revealing that added swarms approximate normalized histograms very effectively. Moreover, the proposed swarm-based algorithm allows for modelling the same statistical data with a lower number of summands in GMM. Full article
(This article belongs to the Special Issue Intelligent Video Analytics)
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17 pages, 19411 KiB  
Article
An Anchor-Free Siamese Network with Multi-Template Update for Object Tracking
by Tongtong Yuan, Wenzhu Yang, Qian Li and Yuxia Wang
Electronics 2021, 10(9), 1067; https://doi.org/10.3390/electronics10091067 - 30 Apr 2021
Cited by 4 | Viewed by 1933
Abstract
Siamese trackers are widely used in various fields for their advantages of balancing speed and accuracy. Compared with the anchor-based method, the anchor-free-based approach can reach faster speeds without any drop in precision. Inspired by the Siamese network and anchor-free idea, an anchor-free [...] Read more.
Siamese trackers are widely used in various fields for their advantages of balancing speed and accuracy. Compared with the anchor-based method, the anchor-free-based approach can reach faster speeds without any drop in precision. Inspired by the Siamese network and anchor-free idea, an anchor-free Siamese network (AFSN) with multi-template updates for object tracking is proposed. To improve tracking performance, a dual-fusion method is adopted in which the multi-layer features and multiple prediction results are combined respectively. The low-level feature maps are concatenated with the high-level feature maps to make full use of both spatial and semantic information. To make the results as stable as possible, the final results are obtained by combining multiple prediction results. Aiming at the template update, a high-confidence multi-template update mechanism is used. The average peak to correlation energy is used to determine whether the template should be updated. We use the anchor-free network to implement object tracking in a per-pixel manner, which computes the object category and bounding boxes directly. Experimental results indicate that the average overlap and success rate of the proposed algorithm increase by about 5% and 10%, respectively, compared to the SiamRPN++ algorithm when running on the dataset of GOT-10k (Generic Object Tracking Benchmark). Full article
(This article belongs to the Special Issue Intelligent Video Analytics)
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