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Study on 3D Action Recognition Based on Deep Neural Network

机译:基于深度神经网络的3D动作识别研究

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Recognition of the detailed human action in a harsh environment such as low light environment is an issue that needs to be constantly resolved in a video surveillance system. Action recognition technology using 3D depth map information is understood as a way to solve this issue. In this paper, we propose efficient action recognition using 3D image based on convolution neural network (CNN). And we apply it to the intelligent video surveillance system and measure the detection accuracy by the action. Experimental results show that the security action recognition accuracy of 61.5%.
机译:在恶劣的环境(例如弱光环境)中识别详细的人类动作是需要在视频监视系统中不断解决的问题。使用3D深度图信息的动作识别技术被认为是解决此问题的一种方法。在本文中,我们提出了基于卷积神经网络(CNN)的使用3D图像的有效动作识别。并将其应用于智能视频监控系统,并通过动作来测量检测精度。实验结果表明,安全动作识别的准确率为61.5%。

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