ArticleAnimals : an open access journal from MDPI2024
MCP: Multi-Chicken Pose Estimation Based on Transfer Learning.
Article in Animals : an open access journal from MDPI, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Occlusion-Robust Cattle Pose Estimation for Precision Livestock Monitoring Using Hierarchical Locality Refinement.Animals : an open access journal from MDPI · 2026Article
- Health Status Recognition of Yellow-Feathered Broilers in Floor-Rearing Environments via Whole-Body and Comb Feature Fusion.Animals : an open access journal from MDPI · 2026Article
- Enhanced Methodology and Experimental Research for Caged Chicken Counting Based on YOLOv8.Animals : an open access journal from MDPI · 2025Article
- The Posture Detection Method of Caged Chickens Based on Computer Vision.Animals : an open access journal from MDPI · 2024Article
- Visual Navigation of Caged Chicken Coop Inspection Robot Based on Road Features.Animals : an open access journal from MDPI · 2024Article
- A Study on the 3D Reconstruction Strategy of a Sheep Body Based on a Kinect v2 Depth Camera Array.Animals : an open access journal from MDPI · 2024Article
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Authors and funding
6 authors.
Funding
Abstract
Poultry managers can better understand the state of poultry through poultry behavior analysis. As one of the key steps in behavior analysis, the accurate estimation of poultry posture is the focus of this research. This study mainly analyzes a top-down pose estimation method of multiple chickens. Therefore, we propose the "multi-chicken pose" (MCP), a pose estimation system for multiple chickens through deep learning. Firstly, we find the position of each chicken from the image via the chicken detector; then, an estimate of the pose of each chicken is made using a pose estimation network, which is based on transfer learning. On this basis, the pixel error (PE), root mean square error (RMSE), and image quantity distribution of key points are analyzed according to the improved chicken keypoint similarity (CKS). The experimental results show that the algorithm scores in different evaluation metrics are a mean average precision (mAP) of 0.652, a mean average recall (mAR) of 0.742, a percentage of correct keypoints (PCKs) of 0.789, and an RMSE of 17.30 pixels. To the best of our knowledge, this is the first time that transfer learning has been used for the pose estimation of multiple chickens as objects. The method can provide a new path for future poultry behavior analysis.
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