Division of Information and Computing Technology, College of Science and Engineering, Hamad Bin Khalifa University, Doha P.O. Box 34110, Qatar.
Visual Computing Group, Microsoft Research Asia, Beijing 100080, China.
Sensors (Basel). 2022 Aug 24;22(17):6348. doi: 10.3390/s22176348.
A wireless vision sensor network (WVSN) is built by using multiple image sensors connected wirelessly to a central server node performing video analysis, ultimately automating different tasks such as video surveillance. In such applications, a large deployment of sensors in the same way as Internet-of-Things (IoT) devices is required, leading to extreme requirements in terms of sensor cost, communication bandwidth and power consumption. To achieve the best possible trade-off, we propose in this paper a new concept that attempts to achieve image compression and early image recognition leading to lower bandwidth and smart image processing integrated at the sensing node. A WVSN implementation is proposed to save power consumption and bandwidth utilization by processing only part of the acquired image at the sensor node. A convolutional neural network is deployed at the central server node for the purpose of progressive image recognition. The proposed implementation is capable of achieving an average recognition accuracy of 88% with an average confidence probability of 83% for five subimages, while minimizing the overall power consumption at the sensor node as well as the bandwidth utilization between the sensor node and the central server node by 43% and 86%, respectively, compared to the traditional sensor node.
无线视觉传感器网络（WVSN）是通过使用多个图像传感器无线连接到中央服务器节点来构建的，该节点执行视频分析，最终实现视频监控等不同任务的自动化。在这种应用中，需要像物联网（IoT）设备一样大量部署传感器，这导致传感器成本、通信带宽和功耗方面的要求极其苛刻。为了实现最佳的权衡，我们在本文中提出了一个新概念，试图在传感节点实现图像压缩和早期图像识别，从而降低带宽并实现智能图像处理。提出了一种 WVSN 实现方案，通过在传感器节点仅处理部分采集的图像来节省功耗和带宽利用。在中央服务器节点部署卷积神经网络，用于渐进式图像识别。所提出的实现方案能够以平均 88%的识别准确率和平均 83%的置信概率处理五个子图像，同时与传统传感器节点相比，将传感器节点和中央服务器节点之间的总功耗和带宽利用率分别降低 43%和 86%。
