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Gewählte Publikation:

Publikationstyp: Zeitschriftenaufsatz
Dokumenttyp: Originalarbeit

Jahr: 2020

AutorInnen: Liu, D; Oczak, M; Maschat, K; Baumgartner, J; Pletzer, B; He, D; Norton, T

Titel: A computer vision-based method for spatial-temporal action recognition of tail-biting behaviour in group-housed pigs.

Quelle: Biosystems Engineering 2020; 195: 27-41

Autor/innen der Vetmeduni Vienna:

Baumgartner Johannes
Maschat Kristina
Oczak Maciej

Beteiligte Vetmed-Organisationseinheiten
Institut für Tierschutzwissenschaften und Tierhaltung

Zugehörige(s) Projekt(e): Österreichisches Kompetenzzentrum für Futter- und Nahrungsmittelqualität, Sicherheit und Innovation

As a typical harmful social behaviour, tail biting is considered to be a welfare-reducing problem with economic consequences for pig production. Taking a computer-vision based approach, in this study, we have developed a novel method to automatically identify and locate tail-biting interactions in group-housed pigs. The method employs a tracking-by-detection algorithm to simplify the group-level behaviour to pairwise interactions. Then, a convolution neural network (CNN) and a recurrent neural network (RNN) are combined to extract the spatial-temporal features and classify behaviour categories. The performance of the proposed method was evaluated by quantifying the localisation accuracy and behaviour classification accuracy. The results demonstrate that the tracking-by-detection approach is capable of obtaining the trajectories of biters and victims with a localisation accuracy of 92.71%. The spatial-temporal features trained by CNN and RNN are robust and effective with a category accuracy of 96.25%. In total, our proposed method is capable to identify and locate 89.23% of tail-biting behaviour in group-housed pigs. (C) 2020 IAgrE. Published by Elsevier Ltd. All rights reserved.

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