Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
6.1
2.6
Journals
Electronics
Special Issues
Advances in Visual Tracking: Emerging Techniques and Applications
share announcement

Advances in Visual Tracking: Emerging Techniques and Applications

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

Deadline for manuscript submissions: 15 December 2025 | Viewed by 2241

Special Issue Editors

Dr. Bo Huang

E-Mail Website
Guest Editor
1. Key Laboratory of Optoelectronic Technology and Systems of the Education Ministry of China, Chongqing University, Chongqing 400044, China
2. College of Optoelectronic Engineering, Chongqing University, Chongqing 400044, China
Interests: machine learning; visual tracking; pattern recognition and deep learning
Dr. Qiao Liu

E-Mail Website
Guest Editor
1. National Center for Applied Mathematics, Chongqing Normal University, Chongqing 401333, China
2. Department of Computer Science and Technology, Harbin Institute of Technology (Shenzhen), Harbin 150001, China
Interests: Visual tracking; image fusion
Dr. Dawei Zhang

E-Mail Website
Guest Editor
Department of Computer Science and Technology, Zhejiang Normal University, Jinhua 321004, China
Interests: deep learning and computer vision; multimodal fusion

Special Issue Information

Dear Colleagues,

Visual tracking is a crucial task in computer vision, with applications ranging from autonomous vehicles and surveillance systems to augmented reality and robotics. Over the past decade, the field has seen rapid advancements driven by deep learning, multimodal data integration, and novel algorithmic approaches. Despite this progress, challenges like occlusion, appearance variations, real-time performance, and tracking in complex environments remain unsolved. This Special Issue aims to bring together recent advancements in visual tracking, highlighting both theoretical innovations and practical implementations.

Topics of interest include, but are not limited to, the following:

(1) Novel Visual Tracking Algorithms: Siamese network-based trackers, correlation filter-based trackers, probabilistic and Bayesian approaches, and hybrid methods.
(2) Robustness in Tracking: Handling occlusion, illumination variations, scale changes, and background clutter.
(3) Multi-Object and Multi-Camera Tracking: Solutions for tracking multiple objects across single or multiple camera setups.
(4) Domain-Specific Tracking Applications: Applications in video surveillance, autonomous driving, human–computer interaction, sports analysis, and medical imaging.
(5) Evaluation and Benchmarking: New metrics, evaluation methodologies, and datasets for assessing tracking performance.
(6) Real-Time and Edge-based Tracking: Optimized algorithms and hardware for real-time and low-power tracking solutions.
(7) Multi-Modal and Sensor Fusion Approaches: Leveraging RGB, depth, infrared, LiDAR, or radar data for enhanced tracking.

Dr. Bo Huang
Dr. Qiao Liu
Dr. Dawei Zhang
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

  • visual tracking
  • siamese network
  • correlation filter
  • domain-specific tracking
  • multi-object tracking
  • benchmark
  • real-time tracking

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

20 pages, 8057 KiB  
Article
Progressive Domain Adaptation for Thermal Infrared Tracking
by Qiao Li, Kanlun Tan, Di Yuan and Qiao Liu
Electronics 2025, 14(1), 162; https://doi.org/10.3390/electronics14010162 - 2 Jan 2025
Viewed by 818
Abstract
Due to the lack of large-scale labeled Thermal InfraRed (TIR) training datasets, most existing TIR trackers are trained directly on RGB datasets. However, tracking methods trained on RGB datasets suffer a significant drop-off in TIR data, due to the domain shift issue. To [...] Read more.
Due to the lack of large-scale labeled Thermal InfraRed (TIR) training datasets, most existing TIR trackers are trained directly on RGB datasets. However, tracking methods trained on RGB datasets suffer a significant drop-off in TIR data, due to the domain shift issue. To address this issue, we propose a Progressive Domain Adaptation framework for TIR tracking (PDAT), which can effectively transfer knowledge from labeled RGB datasets to TIR tracking without requiring a large amount of labeled TIR data. The framework consists of an Adversarial-based Global Domain Adaptation module and a Clustering-based Subdomain Adaptation module to gradually align feature distributions between RGB and TIR domains. Additionally, we collected a large-scale TIR dataset with over 1.48 million unlabeled TIR images for training the proposed domain adaptation framework. Our experimental results on five TIR tracking benchmarks show that the proposed method improves the baseline tracking performance without compromising the tracking speed. Notably, on LSOTB-TIR100, LSOTB-TIR120, and PTB-TIR, the Success rates were approximately 6 percentage points higher than the baseline, demonstrating its effectiveness. Full article
(This article belongs to the Special Issue Advances in Visual Tracking: Emerging Techniques and Applications)
►▼ Show Figures

Figure 1

13 pages, 5146 KiB  
Article
Tracking the Rareness of Diseases: Improving Long-Tail Medical Detection with a Calibrated Diffusion Model
by Tianjiao Zhang, Chaofan Ma and Yanfeng Wang
Electronics 2024, 13(23), 4693; https://doi.org/10.3390/electronics13234693 - 27 Nov 2024
Viewed by 733
Abstract
Motivation: Chest X-ray (CXR) is a routine diagnostic X-ray examination for checking and screening various diseases. Automatically localizing and classifying diseases from CXR as a detection task is of much significance for subsequent diagnosis and treatment. Due to the fact that samples of [...] Read more.
Motivation: Chest X-ray (CXR) is a routine diagnostic X-ray examination for checking and screening various diseases. Automatically localizing and classifying diseases from CXR as a detection task is of much significance for subsequent diagnosis and treatment. Due to the fact that samples of some diseases are difficult to acquire, CXR detection datasets often present a long-tail distribution over different diseases. Objective: The detection performance of tail classes is very poor due to the limited number and diversity of samples in the training dataset and should be improved. Method: In this paper, motivated by a correspondence-based tracking system, we build a pipeline named RaTrack, leveraging a diffusion model to alleviate the tail class degradation problem by aligning the generation process of the tail to the head class. Then, the samples of rare classes are generated to extend the number and diversity of rare samples. In addition, we propose a filtering strategy to control the quality of the generated samples. Results: Extensive experiments on public datasets, Vindr-CXR and RSNA, demonstrate the effectiveness of the proposed method, especially for rare diseases. Full article
(This article belongs to the Special Issue Advances in Visual Tracking: Emerging Techniques and Applications)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-2
Back to TopTop