Single pig pose estimation using cross-stage stacked hourglass network

Posture changes in pig behaviors are a helpful way of detecting early signs of precision livestock farming. In commercial settings, computer vision-based approaches have been widely used to obtain individual pig health and welfare information such as body condition score, live weight, and activity behaviors. For this, precisely estimating pig posture is a prerequisite, which is an important step in obtaining real-time pig information. In this paper, a cross-stage stacked hourglass network is conducted to solve pig posture estimation in a real feedlot environment. In addition, a cross-stage connection method that increases the flow of information to mitigate possible information loss is proposed, which obtains 2.1% improvement of PCKh than that of the original network. We trained and tested the proposed approach on a challenging pig dataset, which includes not only real pig images but also images from different websites. The results indicate that it is possible to recognize pig posture from images without any manual interference automatically, and it has great potential for application in the early detection of health and welfare challenges of commercial pigs.