Computer Vision Based Measurement of Wildfire Smoke Dynamics

This article presents a novel method for measurement of wildfire smoke dynamics based on computer vision and augmented reality techniques. The aspect of smoke dynamics is an important feature in video smoke detection that could distinguish smoke from visually similar phenomena. However, most of the existing smoke detection systems are not capable of measuring the real-world size of the detected smoke regions. Using computer vision and GIS-based augmented reality, we measure the real dimensions of smoke plumes, and observe the change in size over time. The measurements are performed on offline video data with known camera parameters and location. The observed data is analyzed in order to create a classifier that could be used to eliminate certain categories of false alarms induced by phenomena with different dynamics than smoke. We carried out an offline evaluation where we measured the improvement in the detection process achieved using the proposed smoke dynamics characteristics. The results show a significant increase in algorithm performance, especially in terms of reducing false alarms rate. From this it follows that the proposed method for measurement of smoke dynamics could be used to improve existing smoke detection algorithms, or taken into account when designing new ones

Environmental Intelligence Based on Advanced Sensor Networks

systems in an environment, in a way that those highly technological systems become almost its integral part, it is possible to provide additional environment features. Those features are primarily self-monitoring and self-protection, giving the environment rudimentary intelligence and possibility, to operate not only by reaction, but also to operate proactively, having in ''mind' ' its self-protection. In such a way the environment becomes the intelligent environment or more accurately the intelligent selfmonitoring, self-protecting and self-aware environment that reacts on changes and in real time alarms humans responsible for appropriate environment protection actions which will prevent environment further degradation. The paper describes overall architecture of such intelligent environment based on advanced sensor network called the observer network. As an example the system architecture of the forest fire monitoring system is discussed. 1