Overhead spine arch analysis of dairy cows from three-dimensional video

Abdul Jabbar, Khalid; Hansen, Mark F; Smith, Melvyn; Smith, Lyndon

Abstract

We present a spine arch analysis method in dairy cows using overhead 3D video data. This method is aimed for early stage lameness detection. That is important in order to allow early treatment; and thus, reduce the animal suffering and minimize the high forecasted financial losses, caused by lameness. Our physical data collection setup is non-intrusive, covert and designed to allow full automation; therefore, it could be implemented on a large scale or daily basis with high accuracy. We track the animal’s spine using shape index and curvedness measure from the 3D surface as she walks freely under the 3D camera. Our spinal analysis focuses on the thoracic vertebrae region, where we found most of the arching caused by lameness. A cubic polynomial is fitted to analyze the arch and estimate the locomotion soundness. We have found more accurate results by eliminating the regular neck/head movements’ effect from the arch. Using 22-cow data set, we are able to achieve an early stage lameness detection accuracy of 95.4%.

Citation

Abdul Jabbar, K., Hansen, M. F., Smith, M., & Smith, L. (2016, October). Overhead spine arch analysis of dairy cows from three-dimensional video. Paper presented at Eighth International Conference on Graphic and Image Processing (ICGIP 2016)

• Presentation Conference Type: Conference Paper (unpublished)
• Conference Name: Eighth International Conference on Graphic and Image Processing (ICGIP 2016)
• Start Date: Oct 29, 2016
• End Date: Oct 31, 2016
• Acceptance Date: Sep 20, 2016
• Publication Date: Feb 8, 2017
• Peer Reviewed: Peer Reviewed
• Keywords: 3D vision, curvature, dairy cow, early lameness, lameness detection, spine arch, vertebrate
• Publisher URL: http://dx.doi.org/10.1117/12.2266094
• Related Public URLs: http://www.icgip.org/
• Additional Information: Title of Conference or Conference Proceedings : Eighth International Conference on Graphic and Image Processing (ICGIP 2016)