The field of UAV tracking and object detection is rapidly evolving, with a focus on improving accuracy, robustness, and efficiency. Recent developments have explored the use of semantic-aware correlation modeling, multiscale adaptive tracking, and deep learning-based approaches to enhance tracking performance in various scenarios, including nighttime operations and low-light conditions. Additionally, researchers have investigated the application of hybrid deep learning and machine learning methods for waste image classification and fish freshness assessment, achieving state-of-the-art results. Noteworthy papers include: Dynamic Semantic-Aware Correlation Modeling for UAV Tracking, which proposes a dynamic semantic aware correlation modeling tracking framework, and MATrack: Efficient Multiscale Adaptive Tracker for Real-Time Nighttime UAV Operations, which presents a multiscale adaptive system designed specifically for nighttime UAV tracking. These advancements have significant implications for various applications, including disaster rescue, environmental monitoring, logistics transportation, and smart city management.