This paper presents a deep neural network‐based technique to recognize pets' body movements. The motivation to develop the technique arises from the fact that there are an increasing number of animal lovers, say dog or cat lovers, who spend a tremendous amount of effort to take care of their beloved pets, and even to capture the pets' images in daily life and at particular memorable moments for sharing among friends. For illustrative purposes, the recognition model was trained and then tested using a number of cats' images. As it turned out, the model well recognized three body movements: eating, tail raising and yawn, with a recognition accuracy up to 99.45%. Using this recognition technique, pets' images can be captured automatically once specific movements are detected, and job as a pet photographer can be made easy accordingly. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

中文翻译：

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利用深度神经网络开发宠物的身体动作识别技术

本文提出了一种基于深度神经网络的技术来识别宠物的身体动作。开发这种技术的动机来自这样一个事实，即越来越多的动物爱好者，例如狗或猫爱好者，他们花费大量的精力来照顾自己心爱的宠物，甚至是为了捕捉宠物的形象。日常生活，以及在特别难忘的时刻与朋友分享。出于说明目的，训练了识别模型，然后使用许多猫的图像对其进行了测试。事实证明，该模型可以很好地识别出三个身体动作：进食，抬尾和打哈欠，其识别准确率高达99.45％。使用这种识别技术，一旦检测到特定的动作，就可以自动捕获宠物的图像，从而可以轻松地使宠物摄影师工作。©2021日本电气工程师学会。由Wiley Periodicals LLC发布。