Driven by the rapid advancement of Unmanned Aerial Vehicle (UAV) technology, the field of UAV object tracking has witnessed significant progress. This study introduces an innovative single-stream UAV tracking architecture, dubbed NT-Track, which is dedicated to enhancing the efficiency and accuracy of real-time tracking tasks. Addressing the shortcomings of existing tracking systems in capturing temporal relationships between consecutive frames, NT-Track meticulously analyzes the positional changes in targets across frames and leverages the similarity of the surrounding areas to extract feature information. Furthermore, our method integrates spatial and temporal information seamlessly into a unified framework through the introduction of a temporal feature fusion technique, thereby bolstering the overall performance of the model. NT-Track also incorporates a spatial neighborhood feature extraction module, which focuses on identifying and extracting features within the neighborhood of the target in each frame, ensuring continuous focus on the target during inter-frame processing. By employing an improved Transformer backbone network, our approach effectively integrates spatio-temporal information, enhancing the accuracy and robustness of tracking. Our experimental results on several challenging benchmark datasets demonstrate that NT-Track surpasses existing lightweight and deep learning trackers in terms of precision and success rate. It is noteworthy that, on the VisDrone2018 benchmark, NT-Track achieved a precision rate of 90% for the first time, an accomplishment that not only showcases its exceptional performance in complex environments, but also confirms its potential and effectiveness in practical applications.

Active visual perception refers to the ability of a system to dynamically engage with its environment through sensing and action, allowing it to modify its behavior in response to specific goals or uncertainties. Unlike passive systems that rely solely on visual data, active visual perception systems can direct attention, move sensors, or interact with objects to acquire more informative data. This approach is particularly powerful in complex environments where static sensing methods may not provide sufficient information. Active visual perception plays a critical role in numerous applications, including robotics, autonomous vehicles, human-computer interaction, and surveillance systems. However, despite its significant promise, there are several challenges that need to be addressed, including real-time processing of complex visual data, decision-making in dynamic environments, and integrating multimodal sensory inputs. This paper explores both the opportunities and challenges inherent in active visual perception, providing a comprehensive overview of its potential, current research, and the obstacles that must be overcome for broader adoption.

Active visual perception enables an intelligent system to improve perception by controlling how visual evidence is acquired. However, differences in how studies define active perception and evaluate its benefits make approaches difficult to compare across tasks. This review first clarifies the field’s functional scope by examining whether an action is selected for its expected effect on subsequent visual evidence and whether the resulting observation informs a later decision. This definition distinguishes active observation acquisition from processing fixed inputs and guides the selection of representative literature. Within this scope, this review develops an analytical framework organized around four questions. It begins with what evidence the task requires, examines which actions can obtain that evidence and how a candidate observation should be valued, and considers how the next sensing action should be selected and executed. New observations then update the perceptual state and guide subsequent decisions, forming a closed loop that provides a common basis for comparing methods across applications. To examine how their benefits can be demonstrated, this review assesses whether existing datasets support observation selection, how interactive environments enable controlled comparisons, and what physical platforms reveal about execution feasibility. It further examines how baseline comparisons can establish whether adapting observations is necessary and whether informed selection offers benefits beyond simpler sensing strategies. These considerations underpin an evaluation framework that links improvements in visual evidence to downstream task outcomes while accounting for acquisition costs and risks. Its core value is to help isolate the contribution of sensing decisions and determine whether their benefits justify the resources required. The analysis concludes that predicted observation value does not guarantee that useful evidence can be acquired through feasible actions, while task success alone cannot establish the benefit of active observation selection. Further progress requires reliable value estimation and planning over longer decision sequences under physical and resource constraints. It also requires grounding semantic reasoning in acquired evidence and maintaining effective collaboration under imperfect communication, with these capabilities verified through reproducible evaluation of the complete perception and action loop.

Achieving fast and robust object tracking is critical for real-time Unmanned Aerial Vehicle (UAV) applications, where targets often move unpredictably and environmental conditions can rapidly change. In this paper, we propose the Search-Region Adaptive Mamba-ViT Tracker (SAMViTrack), a novel framework that combines the efficiency of Mamba attention with the powerful feature extraction capabilities of Vision Transformer (ViT). Our tracker dynamically adjusts the search region based on the target’s motion and environmental context, ensuring precise tracking even under challenging conditions such as occlusions, fast motion, and scale variations. By integrating an adaptive search mechanism, our SAMViTrack significantly reduces computational overhead without compromising accuracy, making it suitable for real-time deployment on UAVs with limited onboard resources. Extensive experiments on benchmark datasets demonstrate that our method outperforms both traditional and modern trackers, achieving superior accuracy and robustness with improved efficiency. The proposed tracker sets a new baseline, especially by combining Mamba and ViT, for UAV tracking by offering a balance between speed, accuracy, and adaptability in dynamic environments.

Visual tracking has seen remarkable advancements, largely driven by the availability of large-scale training datasets that have enabled the development of highly accurate and robust algorithms. While significant progress has been made in tracking general objects, research on more challenging scenarios, such as tracking camouflaged objects, remains limited. Camouflaged objects, which blend seamlessly with their surroundings or other objects, present unique challenges for detection and tracking in complex environments. This challenge is particularly critical in applications such as military, security, agriculture, and marine monitoring, where precise tracking of camouflaged objects is essential. To address this gap, we introduce the Camouflaged Object Tracking Dataset (COTD), a specialized benchmark designed specifically for evaluating camouflaged object tracking methods. The COTD dataset comprises 200 sequences and approximately 80,000 frames, each annotated with detailed bounding boxes. Our evaluation of 20 existing tracking algorithms reveals significant deficiencies in their performance with camouflaged objects. To address these issues, we propose a novel tracking framework, HiPTrack-MLS, which demonstrates promising results in improving tracking performance for camouflaged objects. COTD and code are avialable at https://github.com/openat25/HIPTrack-MLS.