Authors:
Qinghua Guo
1
;
Dawei Pei
1
;
Yue Sun
1
;
2
;
Patrick P. J. H. Langenhuizen
1
;
Clémence A. E. M. Orsini
3
;
Kristine Hov Martinsen
4
;
Øyvind Nordbø
4
;
J. Bolhuis
3
;
Piter Bijma
3
and
Peter H. N. de With
1
Affiliations:
1
Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
;
2
Faculty of Applied Science, Macao Polytechnic University, Macao Special Administrative Region of China
;
3
Department of Animal Sciences, Wageningen University & Research, Wageningen, The Netherlands
;
4
Norsvin SA, Hamar, Norway
Keyword(s):
Animal Keypoint Detection, Animal Posture Recognition, Multi-Object Surveillance.
Abstract:
Monitoring the daily status of pigs is crucial for enhancing their health and welfare. Pose estimation has emerged as an effective method for tracking pig postures, with keypoint detection and skeleton extraction playing pivotal roles in this process. Despite advancements in human pose estimation, there is limited research focused on pigs. To bridge this gap, this study applies the You Only Look Once model Version 8 (YOLOv8) for keypoint detection and skeleton extraction, evaluated on a manually annotated pig dataset. Additionally, the performance of pose estimation is compared across different data modalities and models, including an image-based model (ResNet-18), a keypoint-based model (Multi-Layer Perceptron, MLP), and a combined image-and-keypoint-based model (YOLOv8-pose). The keypoint detection branch achieves an average Percentage of Detected Joints (PDJ) of 48.96%, an average Percentage of Correct Keypoints (PCK) of 84.85%, and an average Object Keypoint Similarity (OKS) of 8
9.43%. The best overall accuracy obtained for pose estimation is 99.33% by the YOLOv8-pose model, which indicates the superiority of the joint image-keypoint-based model for pose estimation. The conducted comprehensive experiments and visualization results indicate that the proposed method effectively identifies specific pig body parts in most monitoring frames, facilitating an accurate assessment of pig activity and welfare.
(More)