As a typical harmful social behaviour, tail biting is considered to be a welfare-reducing problem with economic consequences for pig production. Taking a computer-vision based approach, in this study, we have developed a novel method to automatically identify and locate tail-biting interactions in group-housed pigs. The method employs a tracking-by-detection algorithm to simplify the group-level behaviour to pairwise interactions. Then, a convolution neural network (CNN) and a recurrent neural network (RNN) are combined to extract the spatial-temporal features and classify behaviour categories. The performance of the proposed method was evaluated by quantifying the localisation accuracy and behaviour classification accuracy. The results demonstrate that the tracking-by-detection approach is capable of obtaining the trajectories of biters and victims with a localisation accuracy of 92.71%. The spatial-temporal features trained by CNN and RNN are robust and effective with a category accuracy of 96.25%. In total, our proposed method is capable to identify and locate 89.23% of tail-biting behaviour in group-housed pigs.

中文翻译：

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基于计算机视觉的群养猪咬尾行为时空动作识别方法

作为一种典型的有害社会行为，咬尾被认为是一个降低福利的问题，对养猪生产造成经济后果。采用基于计算机视觉的方法，在本研究中，我们开发了一种新方法来自动识别和定位群养猪的咬尾相互作用。该方法采用逐检测跟踪算法将组级行为简化为成对交互。然后，将卷积神经网络 (CNN) 和循环神经网络 (RNN) 结合起来，提取时空特征并对行为类别进行分类。通过量化定位精度和行为分类精度来评估所提出方法的性能。结果表明，逐检测跟踪方法能够以 92.71% 的定位精度获得咬伤者和受害者的轨迹。CNN 和 RNN 训练的时空特征稳健有效，类别准确率为 96.25%。总的来说，我们提出的方法能够识别和定位群养猪89.23%的咬尾行为。