Camera Pose Estimation using Human Head Pose Estimation

This paper presents a novel framework for camera pose estimation using the human head as a calibration object. The proposed approach enables extrinsic calibration based on 2D input images (RGB and/or NIR), without any need for additional calibration objects or depth information. The method can be used for single cameras or multi-camera networks. For estimating the human head pose, we rely on a deep learning based 2D human facial landmark detector and fit a 3D head model to estimate the 3D human head pose. The paper demonstrates the feasibility of this novel approach and shows its performance on both synthetic and real multi-camera data. We compare our calibration procedure to a traditional checkerboard calibration technique and calculate calibration errors between camera pairs. Additionally, we examine the robustness to varying input parameters, such as simulated people with different skin tone and gender, head models, and variations in camera positions. We expect our method to be useful in various application domains including automotive incabin monitoring, where the flexibility and ease of handling the calibration procedure are often more important than very high accuracy.