Monocular Person Localization under Camera Ego-motion
This addresses the challenge of accurate person localization under camera ego-motion for robotics applications, representing an incremental improvement over existing methods.
The paper tackles the problem of localizing a person from a moving monocular camera, which is critical for Human-Robot Interaction, by jointly estimating camera attitude and 3D human location through optimization, outperforming baselines in accuracy.
Localizing a person from a moving monocular camera is critical for Human-Robot Interaction (HRI). To estimate the 3D human position from a 2D image, existing methods either depend on the geometric assumption of a fixed camera or use a position regression model trained on datasets containing little camera ego-motion. These methods are vulnerable to fierce camera ego-motion, resulting in inaccurate person localization. We consider person localization as a part of a pose estimation problem. By representing a human with a four-point model, our method jointly estimates the 2D camera attitude and the person's 3D location through optimization. Evaluations on both public datasets and real robot experiments demonstrate our method outperforms baselines in person localization accuracy. Our method is further implemented into a person-following system and deployed on an agile quadruped robot.