In the current surveillance networks, a vast amount of data is generated from different sources, resulting in big data. Such data need intelligent technologies and big data analytics for its realtime analysis to provide different services. This article describes an efficient artificial intelligence and big data analytics-assisted system for real-time fire scene analysis in surveillance networks. The proposed system is based on intelligent independent and subordinate agents, where each agent has a different task to report to the fire brigade and disaster management instantly. A deep yet efficient CNN is utilized in the system for feature extraction, classification, localization, and detection of fire in video frames. When the fire is detected in the frames, a fire alert is instantly sent to the emergency department and all agents immediately start their processing for checking the severity and growth rate of the fire, recognizing the scene and all objects on fire, and evacuation monitoring. Each agent of the system instantly sends information to disaster management to stop the loss of precious human lives and minimizes other economic and ecological loss. Experimental validation shows the promising results compared to existing systems. It is believed that using such a system is the demand of the time to save humanity from massive fire disasters and can make the current surveillance networks more intelligent.

中文翻译：

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智能网络中火场的节能监控

在当前的监视网络中，从不同来源生成大量数据，从而产生了大数据。此类数据需要智能技术和大数据分析才能进行实时分析，以提供不同的服务。本文介绍了一种用于监视网络中实时火灾现场分析的高效人工智能和大数据分析辅助系统。所提出的系统基于智能的独立和下属代理，其中每个代理都有不同的任务要立即向消防队和灾难管理报告。系统中使用了深度却高效的CNN，用于特征提取，分类，定位和视频帧中的火灾检测。当在帧中检测到火灾时，火灾警报立即发送到急诊部门，所有人员立即开始处理，以检查火灾的严重性和增长速度，识别现场和所有着火物体以及疏散监控。该系统的每个代理程序立即将信息发送到灾难管理，以阻止宝贵的生命损失，并最大程度地减少其他经济和生态损失。与现有系统相比，实验验证显示了令人鼓舞的结果。相信使用这样的系统是拯救人类免于大规模火灾的时间的需求，并且可以使当前的监视网络更加智能。该系统的每个代理程序立即将信息发送到灾难管理，以阻止宝贵的生命损失，并最大程度地减少其他经济和生态损失。与现有系统相比，实验验证显示了令人鼓舞的结果。人们相信，使用这种系统是拯救人类免于大规模火灾的时间的需求，并且可以使当前的监视网络更加智能。该系统的每个代理程序立即将信息发送到灾难管理，以阻止宝贵的生命损失，并最大程度地减少其他经济和生态损失。与现有系统相比，实验验证显示了令人鼓舞的结果。相信使用这样的系统是拯救人类免于大规模火灾的时间的需求，并且可以使当前的监视网络更加智能。