Non-Contact Respiration Rate Estimation in Cattle from Overhead Videos

The welfare and productivity of livestock are critical in modern agriculture, requiring automated and noninvasive health and behavior monitoring systems. Such systems can provide information about multiple parameters related to the animal over time. Respiration rate (RR) is one of the vital physiological parameters whose accurate, continuous estimation can provide early detection of diseases and heat stress, as well as an indication of activities such as rumination in cattle. Traditional measurement methods like direct observation and calculation or the use of contact sensors are often labor-intensive or intrusive, highlighting the need for remote solutions. This paper introduces a camera-based, semi-automated method for non-contact respiration rate estimation in cows and calves using a top-down, overhead camera perspective. The proposed pipeline leverages computer vision and signal processing, combining deep learning-based segmentation with contour and area-based analysis to convert subtle flank or abdominal movements into a time-domain signal. This signal is then filtered and analyzed using a peak-picking method for continuous RR estimation. The proposed method is evaluated on video snippets of moderately stationary animals, achieving an approximate maximum estimation error of 5% across four different feature extraction approaches. The results demonstrate the effectiveness of the method and offer encouragement for further investigation.