用姿态估计+双向LSTM，仅需1秒视频就能准确识别奶牛跛行。
Lameness detection in dairy cows using pose estimation and bidirectional LSTMs
- 通过关键点轨迹输入双向LSTM，自动学习运动特征。
- 准确率达85%，优于传统人工特征方法（80%）。
- 适用于短时视频和小数据集，适合实际牧场部署。
本研究提出一种结合姿态估计与双向长短期记忆网络（BLSTM）的奶牛跛行检测方法。利用T-LEAP姿态估计算法从行走视频中提取9个关键点（蹄部、头部和背部）的运动轨迹，作为BLSTM分类器的输入，实现跛行的二分类。该方法无需标记、避免人工特征工程，且能处理短序列与小样本训练数据。实验表明，最优模型分类准确率达85%，显著优于依赖人工设计步态特征的传统方法（80%）。此外，本方法可在仅1秒视频数据下完成跛行检测。
原文摘要 · Abstract (English)
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.
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