Dynamics are important for the recognition of equine pain in video
Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
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.
Original language | English |
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Title of host publication | Proceedings - 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2019 |
Publisher | IEEE |
Publication date | 2019 |
Pages | 12659-12668 |
Article number | 8954474 |
ISBN (Electronic) | 9781728132938 |
DOIs | |
Publication status | Published - 2019 |
Event | 32nd IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2019 - Long Beach, United States Duration: 16 Jun 2019 → 20 Jun 2019 |
Conference
Conference | 32nd IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2019 |
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Land | United States |
By | Long Beach |
Periode | 16/06/2019 → 20/06/2019 |
Series | I E E E Conference on Computer Vision and Pattern Recognition. Proceedings |
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ISSN | 1063-6919 |
- Action Recognition, And Body Pose, Deep Learning, Face, Gesture, Vision Applications and Systems
Research areas
Links
- http://arxiv.org/pdf/1901.02106
Submitted manuscript
ID: 241595534