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一种用于城市交通的智能协作视觉传感器网络。

An Intelligent Cooperative Visual Sensor Network for Urban Mobility.

作者信息

Leone Giuseppe Riccardo, Moroni Davide, Pieri Gabriele, Petracca Matteo, Salvetti Ovidio, Azzarà Andrea, Marino Francesco

机构信息

Institute of Information Science and Technologies, National Research Council of Italy, 56124, Pisa, Italy.

Scuola Superiore Sant'Anna of Pisa, 56124, Pisa, Italy.

出版信息

Sensors (Basel). 2017 Nov 10;17(11):2588. doi: 10.3390/s17112588.

DOI:10.3390/s17112588
PMID:29125535
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC5713638/
Abstract

Smart cities are demanding solutions for improved traffic efficiency, in order to guarantee optimal access to mobility resources available in urban areas. Intelligent video analytics deployed directly on board embedded sensors offers great opportunities to gather highly informative data about traffic and transport, allowing reconstruction of a real-time neat picture of urban mobility patterns. In this paper, we present a visual sensor network in which each node embeds computer vision logics for analyzing in real time urban traffic. The nodes in the network share their perceptions and build a global and comprehensive interpretation of the analyzed scenes in a cooperative and adaptive fashion. This is possible thanks to an especially designed Internet of Things (IoT) compliant middleware which encompasses in-network event composition as well as full support of Machine-2-Machine (M2M) communication mechanism. The potential of the proposed cooperative visual sensor network is shown with two sample applications in urban mobility connected to the estimation of vehicular flows and parking management. Besides providing detailed results of each key component of the proposed solution, the validity of the approach is demonstrated by extensive field tests that proved the suitability of the system in providing a scalable, adaptable and extensible data collection layer for managing and understanding mobility in smart cities.

摘要

智慧城市需要提高交通效率的解决方案，以确保能够最佳地利用城市地区现有的出行资源。直接部署在嵌入式传感器上的智能视频分析技术为收集有关交通和运输的高信息量数据提供了巨大机会，从而能够重建城市出行模式的实时清晰图景。在本文中，我们展示了一个视觉传感器网络，其中每个节点都嵌入了用于实时分析城市交通的计算机视觉逻辑。网络中的节点共享它们的感知，并以协作和自适应的方式对分析场景建立全局和全面的解释。这要归功于一个特别设计的符合物联网（IoT）标准的中间件，它包括网络内事件组合以及对机器对机器（M2M）通信机制的全面支持。通过与车辆流量估计和停车管理相关的两个城市出行示例应用，展示了所提出的协作视觉传感器网络的潜力。除了提供所提出解决方案的每个关键组件的详细结果外，广泛的现场测试证明了该方法的有效性，这些测试证明了该系统适用于为管理和理解智慧城市中的出行提供可扩展、可适应和可扩展的数据收集层。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b8be/5713638/71d05f211c49/sensors-17-02588-g012.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b8be/5713638/71d05f211c49/sensors-17-02588-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b8be/5713638/fe35478d8454/sensors-17-02588-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b8be/5713638/c32e09249377/sensors-17-02588-g002.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b8be/5713638/c98df2a8b839/sensors-17-02588-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b8be/5713638/93e147d340a5/sensors-17-02588-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b8be/5713638/e0bf4cc3305c/sensors-17-02588-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b8be/5713638/0a5e83ceb0da/sensors-17-02588-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b8be/5713638/61b3bd2ce164/sensors-17-02588-g010.jpg
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