Measuring parental care behaviour in the wild is central to the study of animal life-history trade-offs, but is often labour and time-intensive. More efficient machine learning-based video processing tools have recently emerged that allow parental nest visit rates to be measured using video cameras and computer processing. Here, we used open-source software to detect movement events from videos taken at the nest box of a wild passerine bird population. We show that visit numbers from our automatic data collection pipeline strongly correlate with human observations, and predicts an increase in fledglings and recruits in a brood. We further tested other annotation methods on a subset of videos, showing that a machine learning assisted annotation approach can largely increase the accuracy of the obtained measures and cut annotation time significantly compared to a cohort of undergraduate students. Since our automatic pipeline collected biological-meaningful data that would have taken approximately 800 days by human observers, we encourage more researchers to apply existing open-source tools to assist data collection in animal behaviour studies.