A person-following method based on monocular camera for quadruped robots

This paper proposes a person-following method based on monocular vision, which allows quadruped robots to track a target person in both indoor and outdoor environments with different illumination conditions. Our method is composed of a person detector, a Kalman filter (KF) tracker, and a re-identification module. To be more specific, the person detector uses a human pose estimation method to detect persons. The KF is applied to predict the position of the target person and update its state with detection results. A re-identification module is proposed to deal with distractions, where the Convolutional Channel Features (CCF) is used to extract appearance features and Online Boosting is used to distinguish the target person from others. Especially, we design a target recapture mechanism based on the Recurrent Neural Network (RNN). Combining motion information with appearance features, the system can accurately re-identify the target person. Without extra customized markers, our method can track the target person steadily in real-time only using a monocular camera. Experiments results can validate the robustness and effectiveness of the proposed method.