Abstract

The current methods of non-contact livestock body measurement directly deal with the low-quality point cloud data of livestock, which have low robustness and lack practicality. On the one hand, the success rate of keypoint detection for livestock body measurement is low. Due to the severe occlusion and noise in the point cloud data, body measurements of some data cannot be performed. On the other hand, the key frames need to be manually selected from the point cloud sequence during processing. Inspired by the work of 3D reconstruction based on animal statistical shape models, we implement the construction and learning of the statistical shape model of real cattle. Given the establishment of the statistical shape model of cattle, a 3D reconstruction and body measurement approach of real cattle based on low-quality point cloud data is proposed. Nine indicators are calculated and the overall estimation MAPE (Mean Absolute Percentage Error) is 10.27%. The whole process of the body measurement algorithm proposed in our paper can be extended to other quadrupeds.

Details

Title
RAPID AND AUTOMATED BODY MEASUREMENT OF CATTLE BASED ON STATISTICAL SHAPE MODEL
Author
Bao, Y 1 ; H Lu 2 ; J Wu 1 ; Lei, J 1 ; Zhang, J 3 ; Luo, X 1 ; Guo, H 2   VIAFID ORCID Logo 

1  College of Land Science and Technology, China Agricultural University, Beijing 100083, China; College of Land Science and Technology, China Agricultural University, Beijing 100083, China 
2  College of Land Science and Technology, China Agricultural University, Beijing 100083, China; College of Land Science and Technology, China Agricultural University, Beijing 100083, China; College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China 
3  College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China; College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China 
Pages
541-546
Publication year
2023
Publication date
2023
Publisher
Copernicus GmbH
ISSN
21949042
e-ISSN
21949050
Source type
Scholarly Journal
Language of publication
English
DOI
https://doi.org/10.5194/isprs-annals-X-1-W1-2023-541-2023
ProQuest document ID
2898127781
Copyright
© 2023. This work is published under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.