An effective PoseC3D model for typical action recognition of dairy cows based on skeleton features

The precise recognition of dairy cows' motion behaviors is crucial for the intelligent assessment of their health status. Previous researchers have utilized many methods to detect cow’s motion behaviors, but the accuracy and robustness of these methods remains to be improved. Skeleton-based action recognition methods have reported high accuracy and robustness, but these methods have rarely been applied to the detection of cow’s motion behavior. This research adopted a behavior recognition method based on skeleton features of dairy cows. First, the YOLOX was modified to YOLOX-Pose for end-to-end skeleton extraction. According to the extracted information, the PoseC3D based on 3D Convolutional Neural Network (CNN) was used to predict motion behaviors. In the process of model training, PoseC3D utilized a dataset consisting of 400 videos encompassing actions of lying, standing, walking, and lameness, while YOLOX-Pose was trained using a dataset comprising 1800 images. The results showed that, for skeleton extraction, YOLOX-Pose achieved an AP of 96.3% and detection speed of 33.1fps. For the recognition of the four actions using PoseC3D, the Top-1 accuracy was 92.6%. The results showed that the YOLOX-Pose was a lightweight and high-quality pose estimator, and PoseC3D, with extracted 2D skeleton information, could outperform Graph Convolutional Network (GCN) in action detection. The combination of the YOLOX-Pose and PoseC3D could be used to detect the four typical motion behaviors effectively.