Abstract As providing objects that pigs prefer can reduce the occurrence of tail-biting and aggression and consequently improve animal welfare, automatic recognition of pigs’ engagement with different objects can have practical value. Therefore, aim of this study was to develop a computer vision based approach that utilised a recurrent neural network-based deep learning algorithm to recognise pig enrichment engagement (EE) behaviours and preliminarily determine the preference to objects. Two pig pens were studied. 1 day of video was recorded in pen 1, which generated 2400 1 s EE and 2400 1 s non-EE episodes. 80% of these data was randomly selected as training set and the remaining 20% as validation set. Moreover, 4 days of video were recorded and used as the test set in pen 2. Firstly, the HSV (Hue, Saturation, Value) colour space-based tracking algorithm was developed to locate object region of interest. Secondly, the convolutional neural network (CNN) architecture InceptionV3 was used to extract spatial features from each frame. These features were input into the long short-term memory (LSTM) framework to extract spatial-temporal features from each episode. Through the fully connected layer, the prediction function Softmax was finally used to classify these episodes as EE or non-EE behaviour. In the validation set, the proposed algorithm could recognise EE with blue ball, golden ball and wooden beam with an accuracy of 95.2%, 95.4% and 97.3%, respectively. By shortening the radius of the region of interest into a half of the average length of pig body, the corresponding accuracy could be improved into 96.9%, 97.1% and 97.9%, respectively. In the test set, the proposed algorithm could recognise EE with each of these 3 objects with an accuracy of 96.5%, 96.8% and 97.6%, respectively. The proportion of EE with each of these 3 objects was 75.8%, 6.0% and 18.2%, respectively. These results indicate that the proposed method can be used to recognise EE behaviours of pigs, and halving the radius of the region of interest can improve the recognition accuracy of EE behaviours. Moreover, the preference of pigs to objects based on EE duration were preliminarily determined as blue ball > wooden beam > golden ball. The obtained duration of EE behaviours can help farmers to evaluate the enrichment used and thereby to increase the health and welfare of the pigs in their care. Furthermore, the proposed algorithm has reference value for the classification of the behaviours with similar motion patterns.

中文翻译：

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一种用于识别猪与不同富集对象的接触的计算机视觉方法

摘要 由于提供猪喜欢的物体可以减少咬尾和攻击的发生，从而提高动物福利，因此自动识别猪与不同物体的接触具有实用价值。因此，本研究的目的是开发一种基于计算机视觉的方法，该方法利用基于循环神经网络的深度学习算法来识别猪的富集参与 (EE) 行为并初步确定对物体的偏好。研究了两个猪圈。1 号笔录制了 1 天的视频，生成了 2400 1 s EE 和 2400 1 s 非 EE 剧集。这些数据的 80% 被随机选择作为训练集，剩下的 20% 作为验证集。此外，记录了 4 天的视频并用作笔 2 中的测试集。首先，HSV（色相、饱和度、Value）基于颜色空间的跟踪算法被开发来定位感兴趣的对象区域。其次，使用卷积神经网络 (CNN) 架构 InceptionV3 从每一帧中提取空间特征。这些特征被输入到长短期记忆 (LSTM) 框架中，以从每个情节中提取时空特征。通过全连接层，最终使用预测函数 Softmax 将这些情节分类为 EE 或非 EE 行为。在验证集中，所提出的算法可以识别蓝球、金球和木梁的EE，准确率分别为95.2%、95.4%和97.3%。通过将感兴趣区域的半径缩短到猪体平均长度的一半，相应的准确率可以分别提高到96.9%、97.1%和97.9%。在测试集中，所提出的算法可以分别以 96.5%、96.8% 和 97.6% 的准确率识别这 3 个对象中的每一个的 EE。EE 与这 3 个对象中的每一个的比例分别为 75.8%、6.0% 和 18.2%。这些结果表明所提出的方法可用于识别猪的EE行为，并且将感兴趣区域的半径减半可以提高EE行为的识别精度。此外，基于EE持续时间，猪对物体的偏好初步确定为蓝球>木梁>金球。获得的 EE 行为持续时间可以帮助农民评估所使用的增菌，从而提高他们照料猪的健康和福利。此外，