Unmanned Aerial Vehicles (UAVs) have become critical enablers of integrated air-space-ground Internet of Things (IoT) ecosystems, with target tracking serving as a foundational technology. This paper classifies UAV target tracking into two distinct paradigms: active tracking and passive tracking, differentiated by their operational scopes and technical objectives. Active tracking is defined as a closed-loop spatial pursuit system, whereby UAVs dynamically track targets through iterative cycles centered on three primary stages: online passive tracking, state fusion estimation, and tracking strategy generation, with subsequent execution phases implied in the loop. This workflow bridges perception and action, enabling spatial engagement through continuous sensor-to-control feedback. In contrast, passive tracking acts as a vision-centric analytical module that exclusively extracts target image-domain attributes from visual sensors—devoid of physical state inference or control mechanisms. As a preprocessing stage for active systems, it is constrained to the visual perception layer, lacking the spatial engagement capabilities inherent in closed-loop tracking systems. This paper conducts an in-depth analysis of the application, key challenges, and future trends in both active and passive UAV target tracking. By systematically discussing the relationships among relevant technologies, this work aims to establish a foundational reference framework and offer citation material for guiding the future development of UAV target tracking technologies.

In high-altitude scenarios, targets tend to occupy a small number of pixels within the UAV’s field of view, resulting in substantial errors when identity recognition is attempted based solely on appearance features during multi-UAV joint tracking. Existing methodologies typically propose projecting multi-view data onto a single plane and leveraging distance information for identity association; however, their accuracy remains low as they are contingent on one-dimensional target information. To address this limitation, this paper introduces the UAVST-HM (UAV Swarm Tracking in High-altitude scenarios for Multiple targets) model, specifically designed to handle the characteristics of targets in the field of view of multiple UAVs at high altitudes. Initially, we develop techniques for extracting targets’ appearance, geometric, and distribution features. Subsequently, adaptive weights, calculated based on the mean of the respective features, are devised to amalgamate these diverse features, thereby constructing a cost matrix for cross-view target identity matching. This matrix is processed through the Hungarian algorithm, and multi-view target identity association is ultimately achieved via threshold filtering. On the MDMT dataset, our method enhances the MDA indicator, which assesses cross-view target identity matching, by 1.78 percentage points compared to the current state of the art. This significant enhancement substantially improves the overall efficacy of multi-UAV joint visual tracking from a high-altitude perspective.

Cooperative visual tracking involving unmanned aerial vehicles (UAVs) in low-altitude environments is a dynamic and rapidly evolving domain. Existing models encounter challenges with targets, such as scale variation, appearance similarity, and frequent occlusions, which hinder the effective use of target information for cross-view identity association. To address these challenges, this study introduces a model for multi-view, multi-target tracking in low-altitude scenes involving UAVs (MVTL-UAV), an effective multi-target tracking model specifically designed for low-altitude scenarios involving UAVs. The proposed method is built upon existing end-to-end detection and tracking frameworks, introducing three innovative modules: loss reinforcement, coupled constraints, and coefficient improvement. Collectively, these advancements enhance the accuracy of cross-view target identity matching. Our method is trained using the DIVOTrack dataset, which comprises data collected from a single UAV and two handheld cameras. Empirical results indicate that our approach achieves a 2.19% improvement in cross-view matching accuracy (CVMA) and a 1.95% improvement in the cross-view ID F1 metric (CVIDF1) when compared to current state-of-the-art methodologies. Importantly, the model’s performance is improved without compromising computational efficiency, thereby enhancing its practical value in resource-constrained environments. As a result, our model demonstrates satisfactory performance in various low-altitude target tracking scenarios involving UAVs, establishing a new benchmark in this research area.