Emerging Research in Object Tracking and Image Segmentation

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

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

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.

Title: Advanced Network Architecture for Enhanced Object Segmentation and Dehazing in Complex Natural Environments
Authors: Sargis Hovhannisyan; Sos Agaian; Artyom Grigoryan
Affiliation: University of Texas at San Antonio, United States
Abstract: Object segmentation in hazy images, particularly in applications like video surveillance and autonomous driving, is a critical yet challenging task due to low visual quality and varying haze densities. While deep learning has significantly advanced the detection and segmentation of moving objects in single images, video-based approaches offer the advantage of leveraging information from neighboring frames. This is crucial in real-world scenarios where adverse weather conditions like haze and fog complicate accurate object predictions. This article presents a unique network architecture that enhances images and videos by focusing on detecting and segmenting moving and stationary objects under dehazing conditions. Our method has undergone rigorous evaluation on well-established dehazing benchmark video datasets, solar panel segmentation tasks, and moving object segmentation challenges. Extensive experiments on publicly available databases and quantitative and qualitative comparisons of benchmarked dehazing algorithms demonstrate that our algorithm surpasses state-of-the-art synthetic and real-world visual data methods. This research significantly advances image dehazing algorithms and their practical applications in computer vision, particularly improving object segmentation in challenging environmental conditions. It inspires new possibilities in autonomous driving and surveillance systems.