Energy-Efficient Monitoring of Fire Scenes for Intelligent Networks.

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. [ABSTRACT FROM AUTHOR]