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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

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
Publisher or commissioning bodyInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages5
ISBN (Electronic)9781467399616
ISBN (Print)9781467399623
DateAccepted/In press - 6 May 2016
DateE-pub ahead of print - 19 Aug 2016
DatePublished (current) - 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


Conference2016 23rd IEEE International Conference on Image Processing, ICIP 2016
CountryUnited States
CityPhoenix, AZ


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.

    Research areas

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


2016 23rd IEEE International Conference on Image Processing, ICIP 2016

Duration25 Sep 201628 Sep 2016
Location of eventPhoenix Convention Center
CityPhoenix, AZ
CountryUnited States
Degree of recognitionInternational event

Event: Conference

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  • Full-text PDF (accepted author manuscript)

    Rights statement: This is the author accepted manuscript (AAM). The final published version (version of record) is available online via IEEE at Please refer to any applicable terms of use of the publisher.

    Accepted author manuscript, 3 MB, PDF document


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