Abstract
The objective of this paper is the fully automated visual identification of individual Holstein Friesian cattle from dorsal RGB-D imagery taken in real-world farm environments. Autonomous and non-intrusive cattle identification could provide an essential tool for economically-viable machinised farming analytics, social monitoring, cattle traceability, food production management and more. We contribute a dataset and propose a system that can reliably derive animal identities from top-down stills by first depth-segmenting animals in RGB-D frames, and then extracting a subset of local ASIFT coat descriptors predicted as sufficiently individually distinctive across the species. Predictions are generated by a support vector machine (SVM) using radial basis function (RBF) kernels for predictions based on the ASIFT descriptor structure. We show that learning such a species-specific ID-model is effective, and we demonstrate robustness to poor or complex input image conditions such as more than one cow present, bad depth segmentation, etc. The proposed system yields 97% identification accuracy over testing on approximately 86,000 image pair comparisons covering a herd of 40 individuals from the FriesianCattle2015 Dataset.
Original language | English |
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Title of host publication | 2016 IEEE International Conference on Image Process (ICIP 2016) |
Subtitle of host publication | Proceedings of a meeting held 25-29 September 2016, Phoenix, AZ, USA |
Publisher | Institute of Electrical and Electronics Engineers (IEEE) |
Pages | 484-488 |
Number of pages | 5 |
ISBN (Electronic) | 9781467399616 |
ISBN (Print) | 9781467399623 |
DOIs | |
Publication status | Published - Mar 2017 |
Event | 2016 23rd IEEE International Conference on Image Processing, ICIP 2016 - Phoenix Convention Center, Phoenix, AZ, United States Duration: 25 Sept 2016 → 28 Sept 2016 |
Publication series
Name | Proceedings of the IEEE International Conference on Image Processing (ICIP) |
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Publisher | Institute of Electrical and Electronics Engineers (IEEE) |
ISSN (Print) | 2381-8549 |
Conference
Conference | 2016 23rd IEEE International Conference on Image Processing, ICIP 2016 |
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Country/Territory | United States |
City | Phoenix, AZ |
Period | 25/09/16 → 28/09/16 |
Keywords
- Animal Biometrics
- Cattle identification
- ASIFT
- Support Vector Machine
- Hlstein Friesian Cows
Fingerprint
Dive into the research topics of 'Automatic individual holstein friesian cattle identification via selective local coat pattern matching in RGB-D imagery'. Together they form a unique fingerprint.Student theses
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Visual Biometric Processes for Collective Identification of Individual Friesian Cattle
Andrew, W. (Author), Burghardt, T. (Supervisor), Greatwood, C. (Supervisor) & Richards, A. (Supervisor), 7 May 2019Student thesis: Doctoral Thesis › Doctor of Philosophy (PhD)
File
Datasets
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FriesianCattle2015
Burghardt, T. (Creator), Andrew, W. (Creator), Hannuna, S. (Contributor) & Campbell, N. (Contributor), University of Bristol, 21 Sept 2016
DOI: 10.5523/bris.wurzq71kfm561ljahbwjhx9n3, http://data.bris.ac.uk/data/dataset/wurzq71kfm561ljahbwjhx9n3
Dataset
Profiles
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Dr Tilo Burghardt
- School of Computer Science - Associate Professor of Computer Science
- Visual Information Laboratory
- Intelligent Systems Laboratory
Person: Academic , Member