基于YOLO11n姿态估计和手势识别的动态目标人员定位研究OA

To address impact of dense orchard foliage obstructing GNSS signals and affecting positioning navigation,as well as poor ad-aptability and limited flexibility of traditional transport robot follow-up methods,a visual tracking positioning method for transport ro-bots 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 calcu-late spatial coordinates and estimate pose of target individuals.To validate system performance,static hand-raising tests and dynamic re-cognition 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 posi-tioning capabilities.This technological research findings could provide valuable reference for target positioning related studies.