Year: 2025
Type: article
Abstract: Determining behavioral deviations in sheep is challenging through visual observation alone, as sheep are generally stoic animals and may not easily show signs of pain or discomfort. This study aimed to evaluate and classify activity changes in sheep using optical flow algorithms as an alternative approach. Video recordings of 32 sheep housed in 8 pens were analyzed over 4 days, with each day consisting of 4 one-hour video segments. These segments were manually observed at 5-minute intervals to document the time spent laying, standing, and walking. Simultaneously, the same video segments were analyzed using optical flow algorithms to track movement patterns. The calculated optical flow measures, including mean, variance, skewness, and kurtosis, were then correlated with the observed behaviors. These measures demonstrated their potential as effective tools for monitoring behavioral activity changes in sheep, providing an objective method to track behavior and detect deviations that might indicate discomfort, stress, or health issues.
Language: English
Subfield: Small Animals
Field: Veterinary
Domain: Health Sciences
Open Access status: diamond
OpenAlex ID: W4407772876