Lameness detection in dairy cows using pose estimation and bidirectional LSTMs
This work addresses lameness detection for dairy farmers, offering a markerless, automated approach that is incremental over existing methods.
The study tackled lameness detection in dairy cows by combining pose estimation and bidirectional LSTMs, achieving 85% accuracy compared to 80% for a feature-based method and enabling detection with just one second of video data.
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.