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Image quality assessment for intelligent emergency application based on deep neural network

机译：基于深度神经网络的智能应急应用图像质量评估

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摘要

Video surveillance is widely applied in modern intelligent systems, such as access control, pedestrian re-identification. However, with the rapid development of urbanization, urban traffic situation becomes more and more complex. It is a big challenge for video surveillance to cope with such massive data. In addition, existing emergency systems are far from modern requirement. So in this paper, we propose an image quality based framework to improve the performance of video surveillance and design a new urban intelligent emergency system. Specifically, we first analyze emergency evacuation of social group security incident for data acquisition including surveillance video and labels. Then, incorporating image quality assessment and convolution neural network, the dataset can be classified into several parts, and each part demonstrates a particular situation. Afterward, we introduce entropy theory to study the application of urban intelligence emergency. The results show that the research method proposed in this paper effectively obtains the evacuation parameters of evacuation personnel in the evacuation of sudden social group events, and improves the timeliness of information transmission in the evacuation process. The results show that the research method of this paper significantly improves the pertinence, effectiveness and perfection of the emergency plan in the application of urban emergency system. (C) 2019 Published by Elsevier Inc.
机译：视频监控已广泛应用于现代智能系统中，例如访问控制，行人重新识别。但是，随着城市化的快速发展，城市交通状况变得越来越复杂。视频监控应对如此大的数据是一个巨大的挑战。另外，现有的应急系统远非现代要求。因此，在本文中，我们提出了一种基于图像质量的框架来提高视频监控的性能，并设计一种新的城市智能应急系统。具体来说，我们首先分析社会团体安全事件的紧急疏散情况，以获取包括监控视频和标签在内的数据。然后，结合图像质量评估和卷积神经网络，可以将数据集分为几部分，每一部分都展示出一种特殊的情况。随后，我们引入熵理论来研究城市智能应急的应用。结果表明，本文提出的研究方法有效地获得了突发社会群体事件疏散中疏散人员的疏散参数，提高了疏散过程中信息传递的及时性。结果表明，本文的研究方法大大提高了应急预案在城市应急体系中的针对性，有效性和完善性。 （C）2019由Elsevier Inc.发布

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