intrApose: Monocular Driver 6 DOF Head Pose Estimation Leveraging Camera Intrinsics

We present intrApose, a novel method for continuous 6 DOF head pose estimation from a single camera image without prior detection or landmark localization. We argue that using camera intrinsics alongside the intensity information is essential for accurate pose estimation. The proposed head pose estimation framework is crop-aware and scale-aware, i.e., it keeps poses estimated within image cut-outs consistent with the whole image. It employs a continuous, differentiable rotation representation that simplifies the overall architecture compared to existing methods. Our method is validated on
DD-Pose
, a challenging real-world in-vehicle driver observation dataset that offers a broad spectrum of poses and occlusion states from naturalistic driving scenarios. In ablation studies we compare rotation and translation errors of intrinsics-aware and -agnostic methods, continuous and discontinuous rotation representations, and data sampling strategies. Experiments show that leveraging camera intrinsics and a continuous rotation representation (
$\text{SVDO}^+$
) results in a
balanced mean angular error
(BMAE) of 5.8
$^\circ$
compared to the intrinsics agnostic baseline with a discontinuous rotation representation (14.8
$^\circ$
). Furthermore, training with an unbiased data distribution (most driver measurements are close-to-frontal) improved BMAE on the
hard
subset (extreme orientations and occlusions) from 15.3
$^\circ$
to 9.5
$^\circ$
.