Robust Person‐Following Based on Monocular Vision for Elderly‐Care Robot

The Elderly‐Care Robot (ECR) addresses the aging population issue by assisting elderly individuals with recognition, tracking, and companionship, easing the burden on healthcare systems and families. Its person‐following functionality consists of visual tracking and visual servo components. Current visual tracking algorithms face challenges like occlusion and limited computational resources. This paper proposes a robust tracker for embedded systems by combining Kernelized Correlation Filter (KCF) and Kalman Filter (KF). The approach utilizes the Average Peak‐to‐Correlation Energy (APCE) index to detect occlusions and adapt the learning rate accordingly. For visual servoing, an image‐based visual servo control algorithm is developed to estimate the robot's position and guide its movement using monocular vision. Experiments on datasets show improved occlusion handling and real‐time performance with an overlapping success rate of 0.602 on OTB‐100 and 0.247 on LaSOT. In addition, real‐world tests demonstrate the robot's effectiveness in tracking and maintaining distance to a target in an obscured environment with a tracking speed of 0.586 m/s, while maintaining a safe distance of 2.36 m, achieving an 87% success rate.