Department of Art and Design, Shaanxi Fashion Engineering University, Xi'an City 710000, China.
Comput Intell Neurosci. 2022 Jun 15;2022:1279351. doi: 10.1155/2022/1279351. eCollection 2022.
The research aims to improve the comfort and safety of the smart home by adding a motion recognition algorithm to the smart home system. First, the research status of motion recognition is introduced. Second, based on the requirements of the smart home system, a smart home system is designed for middle-aged and elderly users. The software system in this system includes intelligent control subsystems, intelligent monitoring subsystems, and intelligent protection subsystems. Finally, to increase the security of the smart home, the intelligent monitoring subsystem is improved, and an intelligent security subsystem is proposed based on a small-scale motion detection algorithm. The system uses three three-dimensional (3D) convolutional neural networks (CNNs) to extract three image features, so that the data information in the video can be fully extracted. The performance of the proposed intelligent security subsystem based on a small-scale motion detection algorithm is compared and analyzed. The research results show that the accuracy of the system on the University of Central Florida (UCF101) dataset is 94.64%, and the accuracy on the HMDB51 dataset is 90.11%, which is similar to other advanced algorithms. Observing whether there are dangers such as falling inside and outside the family through motion recognition technology has very important application significance for protecting people's personal safety, life, and health.
本研究旨在通过向智能家居系统中添加运动识别算法来提高智能家居的舒适性和安全性。首先,介绍运动识别的研究现状。其次,根据智能家居系统的要求,为中老年用户设计智能家居系统。该系统的软件系统包括智能控制子系统、智能监控子系统和智能保护子系统。最后,为了提高智能家居的安全性,改进了智能监控子系统,并提出了一种基于小规模运动检测算法的智能安全子系统。该系统使用三个三维 (3D) 卷积神经网络 (CNN) 提取三个图像特征,从而充分提取视频中的数据信息。对基于小规模运动检测算法的智能安全子系统的性能进行了比较和分析。研究结果表明,该系统在佛罗里达中央大学 (UCF101) 数据集上的准确率为 94.64%,在 HMDB51 数据集上的准确率为 90.11%,与其他先进算法相似。通过运动识别技术观察家庭内外是否存在危险,如跌倒等,对于保护人们的人身安全、生命和健康具有非常重要的应用意义。