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Publication - Dr Sion Hannuna

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

    Citation

    Andrew, W, Hannuna, SL, Campbell, NW & 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. Institute of Electrical and Electronics Engineers (IEEE), pp. 484-488

    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.

    Full details in the University publications repository