Adaptive Monitoring Relevance in Camera Networks for Critical Surveillance Applications

Camera networks have been considered for a large set of visual monitoring applications. For some of them, cameras may be continuously monitoring scenes or groups of targets, but some events may trigger a critical level of visual monitoring, as in public security, industrial automation, and response to natural disasters. In such way, some critical events as a bomb explosion, a volcanic eruption, forest wildfire, or a car accident must be captured with high relevance, potentially helping when identifying responsibilities and during rescue operations. New relevance levels, which can be reflected in higher quality of transmitted images or video streams or even higher priority during transmission over the network must be quickly assigned to cameras that can view the critical events. In this work we propose a methodology to dynamically assign relevancies to cameras that view the area of critical events, employing scalar sensors and a decentralized decision mechanism. The resulting multimodal camera network can considerably enhance the critical surveillance in different monitoring applications. We propose some optimizations that exploit the monitoring relevance in such scenarios