Daily behavior recognition of cattle based on dynamic region image features in open environment

Rao Fu, Jiandong Fang, Yudong Zhao
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Abstract

In order to recognize the daily behaviors of cattle in an open environment, the daily behaviors of cattle were classified based on the image features in the dynamic region. Firstly, the target detection model was used to locate the cattle feature parts in the dynamic region of the image, and the image features in the dynamic region were extracted according to the label information of the feature parts, then, the deep neural network was used to classify the image features. Finally, the results show that in the open environment, the accuracy of the model in predicting the feeding, lying and standing behaviors of cattle was 84%.
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开放环境下基于动态区域图像特征的牛日常行为识别
为了识别开放环境下牛的日常行为,基于动态区域的图像特征对牛的日常行为进行分类。首先利用目标检测模型定位图像动态区域的牛特征部位,根据特征部位的标签信息提取动态区域的图像特征,然后利用深度神经网络对图像特征进行分类。结果表明,在开放环境下,该模型预测牛的进食、躺卧和站立行为的准确率为84%。
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