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Advances in Object Tracking and Computer Vision

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: closed (15 April 2026) | Viewed by 2040

Special Issue Editors


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Guest Editor
Department of Psychiatry, Harvard Medical School, Harvard University, 25 Shattuck Street, Boston, MA 02115, USA
Interests: computer vision; generative model; image restoration; large language model
Department of Information Technology and Electrical Engineering, ETH Zurich, Gloriastrasse 35, 8092 Zurich, Switzerland
Interests: computer vision; medical image analysis; image registration; image segmentation

Special Issue Information

Dear Colleagues,

Object tracking is a fundamental component of many computer vision applications, such as video surveillance, autonomous driving, human–computer interaction, and augmented reality. Its core objective is to locate and follow objects of interest across frames in a video sequence, which requires understanding object motion dynamics and adapting to various visual transformations. Over the past few decades, a wide range of tracking algorithms have been developed, from classical methods such as Kalman filters and optical flow to modern, deep learning-based techniques that leverage convolutional neural networks (CNNs), recurrent networks, transformers, and even large language models. As new challenges emerge and computational capabilities evolve, object tracking continues to be a vibrant and critical area of research.

This Special Issue aims to showcase cutting-edge research on both traditional and emerging methods in object tracking. We welcome original research articles and reviews that explore theoretical advancements, methodological innovations, and practical applications.

Topics of interests include, but are not limited to:

  • Robust object tracking in complex environments;
  • Multi-object tracking;
  • Object tracking with image/video enhancement;
  • Tracking with non-rigid and deformable objects;
  • Long-term object tracking;
  • Cross-modality tracking (e.g., combining RGB images, depth sensors, lidar, or thermal images);
  • Tracking for autonomous systems and robotics;
  • Applications in healthcare and medical imaging;
  • Performance optimization for real-time tracking;
  • Benchmarking and evaluation of tracking algorithms;
  • Tracking for augmented and virtual reality (AR/VRvr).

Dr. Jiezhang Cao
Dr. Xia Li
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 250 words) can be sent to the Editorial Office for assessment.

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
  • object tracking
  • multi-object tracking
  • long-term object tracking
  • image segmentation
  • image/video enhancement

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Published Papers (1 paper)

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Research

15 pages, 1557 KB  
Article
A Dual-Structured Convolutional Neural Network with an Attention Mechanism for Image Classification
by Yongzhuo Liu, Jiangmei Zhang, Haolin Liu and Yangxin Zhang
Electronics 2025, 14(19), 3943; https://doi.org/10.3390/electronics14193943 - 5 Oct 2025
Cited by 1 | Viewed by 1633
Abstract
This paper presents a dual-structured convolutional neural network (CNN) for image classification, which integrates two parallel branches: CNN-A with spatial attention and CNN-B with channel attention. The spatial attention module in CNN-A dynamically emphasizes discriminative regions by aggregating channel-wise information, while the channel [...] Read more.
This paper presents a dual-structured convolutional neural network (CNN) for image classification, which integrates two parallel branches: CNN-A with spatial attention and CNN-B with channel attention. The spatial attention module in CNN-A dynamically emphasizes discriminative regions by aggregating channel-wise information, while the channel attention mechanism in CNN-B adaptively recalibrates feature channel importance. The extracted features from both branches are fused through concatenation, enhancing the model’s representational capacity by capturing complementary spatial and channel-wise dependencies. Extensive experiments on a 12-class image dataset demonstrate the superiority of the proposed model over state-of-the-art methods, achieving 98.06% accuracy, 96.00% precision, and 98.01% F1-score. Despite a marginally longer training time, the model exhibits robust convergence and generalization, as evidenced by stable loss curves and high per-class recognition rates (>90%). The results validate the efficacy of dual attention mechanisms in improving feature discrimination for complex image classification tasks. Full article
(This article belongs to the Special Issue Advances in Object Tracking and Computer Vision)
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