Cattle Pose Classification System Using DeepLabCut and SVM Model

This paper proposes the cattle pose classification system by using video-based tracking result. The proposed system is composed of two processes namely feature extraction and classification. In the feature extraction, we employ DeepLabCut network to obtain location feature points which are to be combined with cattle bounding box region values. For classification process, the SVM (Support Vector Machine) classifier will be used. To confirm the proposed method, we tested some experimental results by using the video sequences taken in some real-life dairy farms and classify six poses such as “standing”, “sitting”, “eating”, “drinking”, “sitting with leg extend” and “tail raised”. We got average 88.75% accuracy for all poses.