Study of Pose Estimation Based on Spatio-Temporal Characteristics of Cow Skeleton

Abstract

The pose of cows reflects their body condition, and the information contained in the skeleton can provide data support for lameness, estrus, milk yield, and contraction behavior detection. This paper presents an algorithm for automatically detecting the condition of cows in a real farm environment based on skeleton spatio-temporal features. The cow skeleton is obtained by matching Partial Confidence Maps (PCMs) and Partial Affinity Fields (PAFs). The effectiveness of skeleton extraction was validated by testing 780 images for three different poses (standing, walking, and lying). The results indicate that the Average Precision of Keypoints (APK) for the pelvis is highest in the standing and lying poses, achieving 89.52% and 90.13%, respectively. For walking, the highest APK for the legs was 88.52%, while the back APK was the lowest across all poses. To estimate the pose, a Multi-Scale Temporal Convolutional Network (MS-TCN) was constructed, and comparative experiments were conducted to compare different attention mechanisms and activation functions. Among the tested models, the CMS-TCN with Coord Attention and Gaussian Error Linear Unit (GELU) activation functions achieved precision, recall, and F1 scores of 94.71%, 86.99%, and 90.69%, respectively. This method demonstrates a relatively high detection rate, making it a valuable reference for animal pose estimation in precision livestock farming.

A Review on Information Technologies Applicable to Precision Dairy Farming: Focus on Behavior, Health Monitoring, and the Precise Feeding of Dairy Cows

“…The proposed method effectively distinguished the lying, standing, and walking behaviors of dairy cows in natural scenes, and the recognition accuracy reached 95.00% [43]. Wei et al (2023) presented a pose estimation method for cows based on the spatiotemporal features of the skeleton, and they observed that the average precision of the key points (APK) for the pelvis in the standing and lying poses achieved 89.52% and 90.13%, respectively, which validated the effectiveness of skeleton extraction to estimate the pose of cows [44].…”