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Dynamics are Important for the Recognition of Equine Pain in Video

2019-01-07
Sofia Broomé, Karina Bech Gleerup, Pia Haubro Andersen, Hedvig Kjellström

Abstract

A prerequisite to successfully alleviate pain in animals is to recognize it, which is a great challenge in non-verbal species. Furthermore, prey animals such as horses tend to hide their pain. In this study, we propose a deep recurrent two-stream architecture for the task of distinguishing pain from non-pain in videos of horses. Different models are evaluated on a unique dataset showing horses under controlled trials with moderate pain induction, which has been presented in earlier work. Sequential models are experimentally compared to single-frame models, showing the importance of the temporal dimension of the data, and are benchmarked against a veterinary expert classification of the data. We additionally perform baseline comparisons with generalized versions of state-of-the-art human pain recognition methods. While equine pain detection in machine learning is a novel field, our results surpass veterinary expert performance and outperform pain detection results reported for other larger non-human species.

Abstract (translated by Google)
URL

http://arxiv.org/abs/1901.02106

PDF

http://arxiv.org/pdf/1901.02106


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