Emerging Research in Object Tracking and Image Segmentation

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: 31 March 2025 | Viewed by 1245

Special Issue Editors


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Guest Editor
School of Computer Science and Technology, Nanjing University of Information Science & Technology, Nanjing 210044, China
Interests: object tracking; image segmentation
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Department of Science and Technology, University of Naples Parthenope, 80133 Napoli, Italy
Interests: machine learning; kernel methods; lustering; intrinsic dimension estimation; gesture recognition; handwriting recognition; time series prediction; dimensionality reduction
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Visual object tracking and segmentation are both essential components of perception, and taken together they have become an active research topic in the computer vision community over the decades. Visual object tracking and segmentation algorithms have developed rapidly thanks to the massive amount of video data that in turn creates high demand for the speed and accuracy of tracking algorithms. Researchers are motivated to design faster and better methods in spite of the challenges that exist in visual object tracking and segmentation, especially robustness when it comes to heavy occlusions, fast motion, accurate localization, mult-object tracking, and low-resolution. Despite the success in addressing numerous challenges under a wide range of circumstances, the core problems remain complex and challenging.

This main aim of this Special lssue will be to focus on the most recent advancements and trends in visual object tracking and segmentation. Methods such as those reported in the formulation of Siamese networks and spatial-temporal memory for VOT and VOS may be further explored to improve performance. We invite original research work involving novel technigues, innovative methods, and useful applications that lead to significant advances in VOT and VOS. We also welcome reviews and surveys on state-of-the-art methods. Topics of interest include, but are not limited to:

  1. Object detection, identification, recognition, tracking, and segmentation.
  2. Video analysis and tracking.
  3. Image and video enhancement algorithms to improve the quality of video object tracking.
  4. Computational photography and imaging for advanced object detection and tracking.
  5. Depth estimation and three-dimensional reconstruction for augmented reality (AR) and/or advanced driver assistance systems (ADAS).
  6. Learning data representation from video based on supervised, unsupervised, and semi-supervised learning.
  7. Dataset and performance evaluation, person re-identification, and vehicle re-identification.
  8. Human behavior detection, human pose estimation, and tracking.
  9. Visual surveillance and monitoring.

Prof. Dr. Kaihua Zhang
Dr. Francesco Camastra
Guest Editors

Manuscript Submission Information

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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. Information 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

  • visual object tracking
  • image segmentation
  • pose estimation
  • image and video enhancement

Published Papers (1 paper)

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Research

25 pages, 9121 KiB  
Article
Flying Projectile Attitude Determination from Ground-Based Monocular Imagery with a Priori Knowledge
by Huamei Chen, Zhigang Zhu, Hao Tang, Erik Blasch, Khanh D. Pham and Genshe Chen
Information 2024, 15(4), 201; https://doi.org/10.3390/info15040201 - 4 Apr 2024
Viewed by 763
Abstract
This paper discusses using ground-based imagery to determine the attitude of a flying projectile assuming prior knowledge of its external geometry. It presents a segmentation-based approach to follow the object and evaluates it quantitatively with simulated data and qualitatively with both simulated and [...] Read more.
This paper discusses using ground-based imagery to determine the attitude of a flying projectile assuming prior knowledge of its external geometry. It presents a segmentation-based approach to follow the object and evaluates it quantitatively with simulated data and qualitatively with both simulated and real data. Two experimental cases are considered: One assumes reliable target distance measurement from an auxiliary range sensor, while the other assumes no range information. The results show that in the case of an unknown projectile–camera distance, with projectile dimensions of 1.378 m and 0.08 m in length and diameter, the estimated distance, in-plane location, and pitch angle accuracies are about 50 m, 0.15 m, and 6 degrees, respectively. Yaw angle estimation is ambiguous. In the second case, assuming that the projectile–camera distance is known resolves the ambiguity of yaw estimation, resulting in accuracies of about 0.15 m, 3 degrees, and 20 degrees for in-plane location, pitch, and yaw angles, respectively. These accuracies were normalized to a 1-km projectile–camera distance. Full article
(This article belongs to the Special Issue Emerging Research in Object Tracking and Image Segmentation)
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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Title: A Comprehensive Survey on the Effectiveness of Sharpness Aware Minimization and its Progressive Derivative
Authors: Chen-Chien James Hsu
Affiliation: National Taiwan Normal University
Abstract: With the ongoing development of larger and more sophisticated AI models to improve performance, the challenge persists in effectively preventing overfitting of overparametrized Neural Networks to training data. Despite the presence of various regularization techniques aimed at mitigating this issue, poor generalization remains a concern, especially when handling diverse and limited data. This paper explores one of the latest and most promising strategies to address this challenge, Sharpness Aware Minimization (SAM), which concurrently minimizes loss value and sharpness-related loss. While this method exhibits substantial effectiveness, it comes with a notable trade-off in increased training time. Consequently, several variants of SAM have emerged to alleviate these limitations and bolster model performance. This survey paper critically examines the significant advancements achieved by SAM, delves into its constraints, categorizes recent progressive variants, and identifies avenues for further progress to augment current State-of-the-Art results.

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