FI-CAP: Robust Framework to Benchmark Head Pose Estimation in Challenging Environments
Sumit Jha, Carlos Busso · 2018
Head pose estimation is challenging in a naturalistic environment. To effectively train machine-learning algorithms, we need datasets with reliable ground truth labels from diverse environments. We present Fi-Cap, a helmet with fiducial markers designed for head pose estimation. The relative position and orientation of the tags from a reference camera can be automatically obtained from a subset of the tags. Placed at the back of the head, it provides a reference system without interfering with sensors that record frontal face. We quantify the performance of the Fi-Cap by (1) rendering the 3D model of the design, evaluating its accuracy under various rotation, image resolution and illumination conditions, and (2) comparing the predicted head pose with the location of the projected beam of a laser mounted on glasses worn by the subjects in controlled experiments conducted in our laboratory. Fi-Cap provides ideal benchmark information to evaluate automatic algorithms and alternative sensors for head pose estimation in a variety of challenging environments, including our target application for advanced driver assistance systems (ADAS).