物联网架构下基于背景减法的智能开放式停车场设计

A. Sofwan, Monica Sari Hariyanto, A. Hidayatno, E. Handoyo, M. Arfan, M. Somantri
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引用次数: 2

摘要

自上世纪末以来,物联网(IoT)已经发展并渗透到我们的生活中。如今,许多用于任何目的的设备都通过互联网连接在一起。在智能校园环境中,智能节点可以通过计算进出该空间的车辆来检测开放停车位的可用性。该节点采用背景减法,部署在物联网架构中。利用高斯混合模型(Gaussian Mixture Model, GMM)确定前景和背景图像,以检测空旷区域的运动目标。此外,该节点能够以较高的准确率识别车辆类型。车型分类结果由节点通过互联网传输,然后保存到数据服务器。我们观察到设计的系统取得了良好的效果,汽车和摩托车的平均识别准确率分别为93.47%和91.73%。
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Design of Smart Open Parking Using Background Subtraction in the IoT Architecture
The Internet of Things (IoT) has evolved and penetrated to our live since the end of the last century. Nowadays, many devices for any purpose are connected through the Internet. A smart node, in smart campus environment, can detect an availability of an open parking space by calculating the vehicle that enters or outs from the space. The node applies a background subtraction method, which is deployed in IoT architecture. The Gaussian Mixture Model (GMM) is utilized to determine foreground and background image, in order to detect a moving object at an open area. Furthermore, the node can discriminate the type of vehicle with a high accuracy. The result of vehicle type classification is transmitted by the node through the Internet, and then it is saved to the data server. We observe the designed system succeeds delivering a good performance in terms of average accuracy determining car and motorcycle are 93.47% and 91.73%, respectively.
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