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Unmanned aerial vehicle (UAVs) of various shapes and sizes have rapidly emerged, creating an urgent need for a highly transferable, fast, and robust 6-D pose estimation method for visual tasks, such as UAV swarming and monitoring. To address this, this article presents a robust method for estimating the 6-D pose of a UAV relative to an airborne camera based on hybrid features. First, we employ KAPAO3D to simultaneously obtain pose objects using 3-D losses with implicit keypoint associations and keypoint objects predicted based on explicit semantic information. Leveraging prior pose object knowledge enables the establishment of precise 2-D–3-D correspondences, facilitating the creation of fusion objects that alleviate missed detections and false positives in keypoint object predictions. Second, pose objects and fusion objects are, respectively, utilized to construct point and edge constraints to obtain an initial pose. We minimize the 3-D errors of hybrid features with predicted confidence scores to further improve the accuracy. Experimental results demonstrate that our method adapts to various types of UAVs and achieves superior performance, with higher ADD and Te metrics compared with other methods and a running speed of 30 frames/s. This method is expected to have potential practical applications in high-precision real-time tracking tasks.

Capturing the motion of humans without any markers in a moving scene is a challenging issue. Typical methods, such as those based on fixed cameras or wearable sensors, have limits on the measurement range and the number of targets. Therefore, we propose a markerless motion-capture technique to estimate the 3-D pose of humans through a multiunmanned aerial vehicle (UAV) system. By utilizing the advantage of the large observation baselines between UAVs in the system, an algorithm to estimate the pose of airborne cameras and establish the sparse points cloud of background in overlapping view simultaneously was presented. A $3\sigma $ rule based on reprojection error is employed to eliminate the incorrect conjugate keypoint pairs, enhancing the precision of pose estimation for airborne cameras. To verify the accuracy of the developed technique, an indoor test was conducted to compare it with a commercial capture system, the results show that the average measurement error of the 3-D joints of humans is less than 30 mm. Furthermore, we demonstrate the feasibility of our approach in outdoor scenes, including multiperson scenarios and moving scenarios.