•We propose a robust registration method for real-time head tracking in the MR scanner with high temporal resolution.•We aggregate motion estimates into a per-sequence average score and show that motion affects T1-weighted image quality, even for predominantly healthy, compliant participants.•In three evaluations, our registration method outperforms the vendor-supplied method with increased similarity to fMRI motion traces, improved recovery of an independently recorded breathing signal, and higher correlation with structural MRI quality estimates.•We present a strong association of increased motion with increasing age and body mass index, as well as longitudinally with scan session duration in the Rhineland study - a large population cohort.•We observe a high correlation of motion scores across sequences, hinting at the possibility to employ fMRI derived motion scores as a surrogate to control motion in structural neuromorphometric statistical analyses. Head motion during MR acquisition reduces image quality and has been shown to bias neuromorphometric analysis. The quantification of head motion, therefore, has both neuroscientific as well as clinical applications, for example, to control for motion in statistical analyses of brain morphology, or as a variable of interest in neurological studies. The accuracy of markerless optical head tracking, however, is largely unexplored. Furthermore, no quantitative analysis of head motion in a general, mostly healthy population cohort exists thus far. In this work, we present a robust registration method for the alignment of depth camera data that sensitively estimates even small head movements of compliant participants. Our method outperforms the vendor-supplied method in three validation experiments: 1. similarity to fMRI motion traces as a low-frequency reference, 2. recovery of the independently acquired breathing signal as a high-frequency reference, and 3. correlation with image-based quality metrics in structural T1-weighted MRI. In addition to the core algorithm, we establish an analysis pipeline that computes average motion scores per time interval or per sequence for inclusion in downstream analyses. We apply the pipeline in the Rhineland Study, a large population cohort study, where we replicate age and body mass index (BMI) as motion correlates and show that head motion significantly increases over the duration of the scan session. We observe weak, yet significant interactions between this within-session increase and age, BMI, and sex. High correlations between fMRI and camera-based motion scores of proceeding sequences further suggest that fMRI motion estimates can be used as a surrogate score in the absence of better measures to control for motion in statistical analyses.