Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2508.10643 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866913991569178624 |
|---|---|
| author | Russello, Helena van der Tol, Rik van Henten, Eldert J. Kootstra, Gert |
| author_facet | Russello, Helena van der Tol, Rik van Henten, Eldert J. Kootstra, Gert |
| contents | This study presents a lameness detection approach that combines pose estimation and Bidirectional Long-Short-Term Memory (BLSTM) neural networks. Combining pose-estimation and BLSTMs classifier offers the following advantages: markerless pose-estimation, elimination of manual feature engineering by learning temporal motion features from the keypoint trajectories, and working with short sequences and small training datasets. Motion sequences of nine keypoints (located on the cows' hooves, head and back) were extracted from videos of walking cows with the T-LEAP pose estimation model. The trajectories of the keypoints were then used as an input to a BLSTM classifier that was trained to perform binary lameness classification. Our method significantly outperformed an established method that relied on manually-designed locomotion features: our best architecture achieved a classification accuracy of 85%, against 80% accuracy for the feature-based approach. Furthermore, we showed that our BLSTM classifier could detect lameness with as little as one second of video data. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_10643 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Lameness detection in dairy cows using pose estimation and bidirectional LSTMs Russello, Helena van der Tol, Rik van Henten, Eldert J. Kootstra, Gert Computer Vision and Pattern Recognition This study presents a lameness detection approach that combines pose estimation and Bidirectional Long-Short-Term Memory (BLSTM) neural networks. Combining pose-estimation and BLSTMs classifier offers the following advantages: markerless pose-estimation, elimination of manual feature engineering by learning temporal motion features from the keypoint trajectories, and working with short sequences and small training datasets. Motion sequences of nine keypoints (located on the cows' hooves, head and back) were extracted from videos of walking cows with the T-LEAP pose estimation model. The trajectories of the keypoints were then used as an input to a BLSTM classifier that was trained to perform binary lameness classification. Our method significantly outperformed an established method that relied on manually-designed locomotion features: our best architecture achieved a classification accuracy of 85%, against 80% accuracy for the feature-based approach. Furthermore, we showed that our BLSTM classifier could detect lameness with as little as one second of video data. |
| title | Lameness detection in dairy cows using pose estimation and bidirectional LSTMs |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2508.10643 |