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.

Published in

IEEE Computer Society Conference on Computer Vision and Pattern Recognition
2019, pages: 12859-12668
Title: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition : proceedings : 16-20 June 2019, Long Beach, California
eISBN: 978-1-7281-3293-8
Publisher: IEEE

Conference

IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), JUN 16-20, 2019, Long Beach, CA

SLU Authors

• Haubro Andersen, Pia

  • Department of Clinical Sciences, Swedish University of Agricultural Sciences
    

UKÄ Subject classification

Clinical Science
Computer Science


Publication identifier

DOI: https://doi.org/10.1109/CVPR.2019.01295

Permanent link to this page (URI)

https://res.slu.se/id/publ/106951