Measuring parental care behaviour in the wild is central to the study of animal ecology and evolution, but it is often labour‐ and time‐intensive. Efficient open‐source tools have recently emerged that allow animal behaviour to be quantified from videos using machine learning and computer vision techniques, but there is limited appraisal of how these tools perform compared to traditional methods. To gain insight into how different methods perform in extracting data from videos taken in the field, we compared estimates of the parental provisioning rate of wild house sparrows Passer domesticus from video recordings. We compared four methods: manual annotation by experts, crowd‐sourcing, automatic detection based on the open‐source software DeepMeerkat, and a hybrid annotation method. We found that the data collected by the automatic method correlated with expert annotation (r = 0.62) and further show that these data are biologically meaningful as they predict brood survival. However, the automatic method produced largely biased estimates due to the detection of non‐visitation events, while the crowd‐sourcing and hybrid annotation produced estimates that are equivalent to expert annotation. The hybrid annotation method takes approximately 20% of annotation time compared to manual annotation, making it a more cost‐effective way to collect data from videos. We provide a successful case study of how different approaches can be adopted and evaluated with a pre‐existing dataset, to make informed decisions on the best way to process video datasets. If pre‐existing frameworks produce biased estimates, we encourage researchers to adopt a hybrid approach of first using machine learning frameworks to preprocess videos, and then to do manual annotation to save annotation time. As open‐source machine learning tools are becoming more accessible, we encourage biologists to make use of these tools to cut annotation time but still get equally accurate results without the need to develop novel algorithms from scratch.

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