Dynamic target individuals localization based on YOLO11n pose estimation and gesture recognition

To address impact of dense orchard foliage obstructing GNSS signals and affecting positioning navigation, as well as poor adaptability and limited flexibility of traditional transport robot follow-up methods, a visual tracking positioning method for transport robots was proposed．By integrating gesture recognition within YOLO11n pose estimation framework, a human keypoint recognition and localization model was constructed．Combined with a depth camera, median filtering, and quaternion methods were employed to calculate spatial coordinates and estimate pose of target individuals．To validate system performance, static hand-raising tests and dynamic recognition anti-interference tests were designed．Test results demonstrated that within a range of 1.0 to 2.0 meters from camera, average recognition accuracy exceeded 92.5% during static hand-raising tests．At a distance of 1.5 meters from camera, recognition accuracy reached 90% in dynamic recognition anti-interference test conditions, indicating robust automatic recognition and stable dynamic positioning capabilities．This technological research findings could provide valuable reference for target positioning related studies．