Worker Tracking Using Skeletal Graphs for Agricultural Support Robot in Narrow Furrows

This study proposes an innovative approach for tracking agricultural workers in narrow furrows, aimed at enhancing the performance of agricultural support robots. The method integrates RGB-D camera-based skeleton extraction with a Space-Time-Separable Graph Convolutional Network (STS-GCN) for lower limb motion prediction, and introduces a novel fusion algorithm that combines these predictions with real-time observations. Experimental results demonstrate the proposed method’s ability to maintain accurate worker tracking even in occluded scenarios by complementing observations with predictions. This research provides insights into the effective- ness of combining skeletal graph-based motion prediction with real-time observations for robust worker tracking in challenging agricultural environments.