Behaviour recognition of housed sheep based on spatio-temporal information

Abstract

ABSTRACTLivestock behavior is related to the healthy breeding and welfare level. Therefore, monitoring the behavior of sheep is helpful to predict the health status of sheep and thus safeguard the production performance of sheep. Taking semi-housed sheep as the research object, a behavior identification method for housed sheep based on spatio-temporal information is proposed. Firstly, video acquisition of housed sheep is carried out, and sheep detection and tracking is implemented based on the YOLOv5 coupled with a Deep-SORT algorithm, which detects sheep from the flock and labels their identity information; then, sheep posture estimation is done based on the Alphapose algorithm, which estimates the keypoints in the skeleton; finally, the detected keypoints are inputted into the trained spatio-temporal graph convolutional network model, and the spatio-temporal graph constructed from the keypoints and edge information is used for sheep behavior recognition. In the study, two keypoints selection proposals were made to implement sheep behavior recognition for non-contacted detection of ruminating, lying down and other behaviors. The results show the feasibility of sheep behavior recognition based on spatio-temporal information in semi-housed farming. The method provides a new solution idea for intelligent monitoring of sheep behavior and health assessment.

Keywords

• Semi-housed sheep
• behaviour recognition
• spatio-temporal graph convolutional network
• target detection with YOLOv5
• posture estimation with alpha pose