Automatic individual holstein friesian cattle identification via selective local coat pattern matching in RGB-D imagery

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

9 Citations (Scopus)
438 Downloads (Pure)

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 languageEnglish
Title of host publication2016 IEEE International Conference on Image Process (ICIP 2016)
Subtitle of host publicationProceedings of a meeting held 25-29 September 2016, Phoenix, AZ, USA
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages484-488
Number of pages5
ISBN (Electronic)9781467399616
ISBN (Print)9781467399623
DOIs
Publication statusPublished - Mar 2017
Event2016 23rd IEEE International Conference on Image Processing, ICIP 2016 - Phoenix Convention Center, Phoenix, AZ, United States
Duration: 25 Sep 201628 Sep 2016

Publication series

NameProceedings of the IEEE International Conference on Image Processing (ICIP)
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
ISSN (Print)2381-8549

Conference

Conference2016 23rd IEEE International Conference on Image Processing, ICIP 2016
CountryUnited States
CityPhoenix, AZ
Period25/09/1628/09/16

Keywords

  • Animal Biometrics
  • Cattle identification
  • ASIFT
  • Support Vector Machine
  • Hlstein Friesian Cows

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  • Student Theses

    Visual Biometric Processes for Collective Identification of Individual Friesian Cattle

    Author: Andrew, W., 7 May 2019

    Supervisor: Burghardt, T. (Supervisor), Greatwood, C. (Supervisor) & Richards, A. (Supervisor)

    Student thesis: Doctoral ThesisDoctor of Philosophy (PhD)

    File

    Datasets

    FriesianCattle2015

    Burghardt, T. (Creator), Andrew, W. (Creator), Hannuna, S. L. (Contributor) & Campbell, N. W. (Contributor), University of Bristol, 21 Sep 2016

    Dataset

    Cite this

    Andrew, W., Hannuna, S. L., Campbell, N. W., & Burghardt, T. (2017). Automatic individual holstein friesian cattle identification via selective local coat pattern matching in RGB-D imagery. In 2016 IEEE International Conference on Image Process (ICIP 2016): Proceedings of a meeting held 25-29 September 2016, Phoenix, AZ, USA (pp. 484-488). (Proceedings of the IEEE International Conference on Image Processing (ICIP)). Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/ICIP.2016.7532404