Dynamics Are Important for the Recognition of Equine Pain in Video

Sofia Broome, Karina Bech Gleerup, Pia Haubro Andersen, Hedvig Kjellstrom; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019, pp. 12667-12676

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.

Related Material


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[bibtex]
@InProceedings{Broome_2019_CVPR,
author = {Broome, Sofia and Gleerup, Karina Bech and Andersen, Pia Haubro and Kjellstrom, Hedvig},
title = {Dynamics Are Important for the Recognition of Equine Pain in Video},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2019}
}