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Our laboratory investigates circadian rhythms using animal models and has collected hundreds of hours of video data from sheep over recent years. Despite rapid advances in computer vision, we have identified a key methodological gap: there is currently no integrated, end‑to‑end pipeline that links raw video processing to behavioural pattern discovery, behavioural annotation, and ultimately circadian rhythm analysis. In preliminary work, we piloted a pose‑based behavioural analysis approach to begin addressing this challenge. An overview of our prototype pipeline is available here: https://github.com/user-attachments/assets/49c39c32-7cbc-4f84-a175-7c457dd068d0, along with an example output video here: https://github.com/user-attachments/assets/654c28a5-69b6-4507-94a9-ffb0c72b199e. This project offers students the opportunity to design and implement a scalable, end‑to‑end analysis pipeline for video actigraphy. Students will have access to substantial computational resources via the UWA High Performance Computing Cluster (Kaya), enabling exploration of high‑throughput methods. There is considerable scope for creativity, including experimenting with state‑of‑the‑art computer vision techniques (e.g., pose estimation and foreground detection) and machine learning methods for unsupervised pattern discovery and temporal modelling of behaviour.