通过视频检测马耳部动作，自动识别其情绪状态
Read My Ears! Horse Ear Movement Detection for Equine Affective State Assessment
- 用深度学习与光流法分析马匹视频中的耳朵动作
- 在公开数据集上达到87.5%的耳动判断准确率
- 为动物福利与兽医诊断提供自动化工具支持
马面部动作编码系统（EquiFACS）通过独立的动作单元（AUs）实现面部动作的系统化标注，是评估马匹情绪状态的重要工具，能识别与不适相关的细微面部表情。然而，由于人工标注面部AUs耗时且成本高，相关数据标注资源稀缺，限制了该领域发展。为此，自动化标注系统对利用现有数据集、提升情绪状态检测能力至关重要。本文研究了多种从马匹视频中检测和定位特定耳部动作单元的方法，结合基于深度学习的视频特征提取与循环神经网络进行视频分类，并对比经典光流方法。在公开马匹视频数据集上，实现了87.5%的耳动存在判断准确率，验证了该方法的可行性。文章讨论了未来发展方向，旨在缩小自动化动作检测与实际应用之间的差距。代码将开源发布于 https://github.com/jmalves5/read-my-ears。
原文摘要 · Abstract (English)
The Equine Facial Action Coding System (EquiFACS) enables the systematic annotation of facial movements through distinct Action Units (AUs). It serves as a crucial tool for assessing affective states in horses by identifying subtle facial expressions associated with discomfort. However, the field of horse affective state assessment is constrained by the scarcity of annotated data, as manually labelling facial AUs is both time-consuming and costly. To address this challenge, automated annotation systems are essential for leveraging existing datasets and improving affective states detection tools. In this work, we study different methods for specific ear AU detection and localization from horse videos. We leverage past works on deep learning-based video feature extraction combined with recurrent neural networks for the video classification task, as well as a classic optical flow based approach. We achieve 87.5% classification accuracy of ear movement presence on a public horse video dataset, demonstrating the potential of our approach. We discuss future directions to develop these systems, with the aim of bridging the gap between automated AU detection and practical applications in equine welfare and veterinary diagnostics. Our code will be made publicly available at https://github.com/jmalves5/read-my-ears.
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