Video analytic system for detecting cow structure

Abstract In animal agriculture, animal health directly influences productivity. For dairy cows, many health conditions can be evaluated by trained observers based on visual appearance and movement. However, to manually evaluate every cow in a commercial farm is expensive and impractical. This study introduces a video-analytic system which automatically detects the cow structure from captured video sequences. A side-view cow structural model is designed to describe the spatial positions of the joints (keypoints) of the cow, and we develop a system using deep learning to automatically extract the structural model from videos. The proposed system can detect multiple cows in the same frame and provides robust performance for the body region under practical challenges like obstacles (fences) and poor illumination. Compared to other object detection methods, this system provides better detection results and successfully isolates the body keypoints of each cow even when the cows are close to each other.