Toni Jakovčević

Visual spatial-context based wildfire smoke sensor

Sensors for early fire detection based on visual analysis have been under constant development and improvement, especially during the last decade. However, there is still a lot of room for advancement to increase the accuracy and reliability of such sensors. In this paper, a novel method for wildfire smoke detection based on spatial context analysis as well as motion detection, chromatic, texture and shape analysis is introduced. Several measures for evaluating quality of smoke detection are used, both on image and pixel scale. Smoke is a semi-transparent and amorphous phenomenon whose boundaries are hard to determine precisely; therefore, fuzzy measures are introduced for assessing the detection error. The proposed method is evaluated using the proposed measures and compared with two existing methods. The results show that the wildfire sensor based on proposed method is capable of detecting fire-smoke accurately and reliably, and in most detection aspects it outperforms the existing methods.

Adaptive estimation of visual smoke detection parameters based on spatial data and fire risk index

Standard wildfire smoke detection systems detect fires using remote cameras located at observation posts. Images from the cameras are analyzed using standard computer vision techniques, and human intervention is required only in situations in which the system raises an alarm. The number of alarms depends largely on manually set detection sensitivity parameters. One of the primary drawbacks of this approach is the false alarm rate, which impairs the usability of the system. In this paper, we present a novel approach using GIS and augmented reality to include the spatial and fire risk data of the observed scene. This information is used to improve the reliability of the existing systems through automatic parameter adjustment. For evaluation, three smoke detection methods were improved using this approach and compared to the standard versions. The results demonstrated significant improvement in different smoke detection aspects, including detection range, rate of correct detections and decrease in the false alarm rate.

Vision based wildfire and natural risk observers

Wildfires are natural risk phenomena that cause significant economic and environmental damage. In wildfire fighting strategy it is important to detect the wildfire in its initial stage and to apply, as soon as possible, the most appropriate fire fighting action. In both cases wildfire monitoring and surveillance systems are of great importance, so in the last decade the interest for various wildfire monitoring and surveillance systems has increased, both on the research and the implementation level. This paper describes one such system named iForestFire. It is an example of advanced terrestrial vision based wildfire monitoring and surveillance system, today widely used in various Croatian National and Nature Parks and regions, but it is also a system in constant development and improvement, both on theoretical and practical level. This paper describes its last improvements in video detection part that are based on notation of observer, cogent confabulation theory and mechanism of thought. Inclusion of cogent confabulation theory allows us to expend the use of existing wildfire observers to more general natural risk observers.

A stereo approach to wildfire smoke detection: the improvement of the existing methods by adding a new dimension

In this paper, we present a novel approach to visual smoke detection based on stereo vision. General smoke detection is usually performed by analyzing the images from remote cameras using various computer vision techniques. The literature on smoke detection shows a variety of approaches, and the focus of this paper is the improvement of the general smoke detection process by introducing stereo vision. Two cameras are used to estimate the distance and size of the detected phenomena based on stereo triangulation. Using this information, the minimum size and overall dynamics of the detected regions are further examined to ensure the elimination of false alarms induced by various phenomena (such as the movement of objects located at short distances from the camera). Such false alarms could easily be detected by the proposed stereo system, allowing the increase of the sensitivity and overall performance of the detection. We analyzed the requirements of such system in terms of precision and robustness to possible error sources, especially when dealing with detection of smoke at various distances from the camera. For evaluation, three existing smoke detection methods were tested and the results were compared to their newly implemented stereo versions. The results demonstrated better overall performance, especially a decrease in false alarm rates for all tested methods.