T-LEAP：使用时间信息的遮挡鲁棒性行走奶牛姿态估计
T-LEAP: Occlusion-robust pose estimation of walking cows using temporal information
AI总结：
本研究提出一种利用时间信息的奶牛姿态估计模型T-LEAP，通过引入过去帧数据提升遮挡环境下的鲁棒性，实验证明其在遮挡数据上比静态模型最多提升32.9%，且对未知奶牛具有良好泛化能力。
摘要：
随着奶牛场牛群规模的不断扩大，奶牛的自动健康监测日益受到关注。跛行是奶牛中普遍存在的一种健康疾病，通常通过分析奶牛的步态来检测。利用姿态估计模型可以在视频中跟踪奶牛的步态，因为模型能够自动定位图像和视频中的解剖学关键点。大多数动物姿态估计模型是静态的，即视频是逐帧处理的，不使用任何时间信息。在本研究中，我们将用于动物姿态估计的静态深度学习模型扩展为包含过去帧信息的时间模型。我们比较了静态和时间姿态估计模型的性能。数据包含从30头不同奶牛在户外通道行走的视频（30 fps）中提取的1059个样本，每个样本由4个连续帧组成。由于农场环境容易出现遮挡，我们通过向视频中添加人工遮挡来测试静态和时间模型的鲁棒性。实验表明，在无遮挡数据上，静态和时间两种方法均达到了99%的关键点正确率（PCKh@0.2）。在有遮挡数据上，我们的时间方法比静态方法最多高出32.9%，这表明在容易出现遮挡的环境（如奶牛场）中，使用时间数据有利于姿态估计。通过在包含未知奶牛（未出现在训练集中的奶牛）的数据上进行测试，评估了时间模型的泛化能力。结果显示，已知奶牛的平均PCKh@0.2为93.8%，未知奶牛为87.6%，表明该模型能够很好地泛化到新的奶牛，并且可以轻松地针对新牛群进行微调。最后，我们展示了在更困难的任务中，如遮挡和未知奶牛，更深的架构更为有利。
英文摘要：
As herd size on dairy farms continues to increase, automatic health monitoring of cows is gaining in interest. Lameness, a prevalent health disorder in dairy cows, is commonly detected by analyzing the gait of cows. A cow's gait can be tracked in videos using pose estimation models because models learn to automatically localize anatomical landmarks in images and videos. Most animal pose estimation models are static, that is, videos are processed frame by frame and do not use any temporal information. In this work, a static deep-learning model for animal-pose-estimation was extended to a temporal model that includes information from past frames. We compared the performance of the static and temporal pose estimation models. The data consisted of 1059 samples of 4 consecutive frames extracted from videos (30 fps) of 30 different dairy cows walking through an outdoor passageway. As farm environments are prone to occlusions, we tested the robustness of the static and temporal models by adding artificial occlusions to the videos.The experiments showed that, on non-occluded data, both static and temporal approaches achieved a Percentage of Correct Keypoints (PCKh@0.2) of 99%. On occluded data, our temporal approach outperformed the static one by up to 32.9%, suggesting that using temporal data was beneficial for pose estimation in environments prone to occlusions, such as dairy farms. The generalization capabilities of the temporal model was evaluated by testing it on data containing unknown cows (cows not present in the training set). The results showed that the average PCKh@0.2 was of 93.8% on known cows and 87.6% on unknown cows, indicating that the model was capable of generalizing well to new cows and that they could be easily fine-tuned to new herds. Finally, we showed that with harder tasks, such as occlusions and unknown cows, a deeper architecture was more beneficial.
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