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

Vision Based Smoke Detection

Cílem této bakalářské práce je analýza obrazu ze statické kamery a následné zpracování a nalezení oblastí, které obsahují kouř. Prvním krokem je extrakce pozadí pomocí modelu Gaussových směsí (Mixture of Gaussians). Toto pozadí je průběžně aktualizováno, abychom se vyhnuli případným změnám ve scéně např. změna světla nebo objektů. Rozdíl aktuálního snímku a pozadí s aplikováným prahováním jsou oblasti, které se ve videosekvenci mění. Detekce kouře z těchto oblastí provádíme porovnáváním jednotlivých barevných složek a kontrolou pohybu a změny velikostí kontur oblastí, ve kterých by se mohl potencionálně vyskytovat kouř.The aim of this bachelor thesis is to analyze the image from a static camera and to subsequently process and to find areas containing smoke. The first step is background extraction using the Mixture of Gaussians. The background is updated many times during the application is running. The difference between the current frame and the applied threshold is the areas that are changing in the movie. Smoke detection from these areas is done by comparing each color component and controlling motion and changing the contour sizes of the areas where smoke might be present.460 - Katedra informatikydobř

Video-based Smoke Detection Algorithms: A Chronological Survey

Over the past decade, several vision-based algorithms proposed in literature have resulted into development of a large number of techniques for detection of smoke and fire from video images. Video-based smoke detection approaches are becoming practical alternatives to the conventional fire detection methods due to their numerous advantages such as early fire detection, fast response, non-contact, absence of spatial limits, ability to provide live video that conveys fire progress information, and capability to provide forensic evidence for fire investigations. This paper provides a chronological survey of different video-based smoke detection methods that are available in literatures from 1998 to 2014.Though the paper is not aimed at performing comparative analysis of the surveyed methods, perceived strengths and weakness of the different methods are identified as this will be useful for future research in video-based smoke or fire detection. Keywords: Early fire detection, video-based smoke detection, algorithms, computer vision, image processing

Machine-Learning Based Smoke Detection

A machine learning based arrangement can be used to more accurately detect whether a smoke alarm should be sounded based on a determined rate of change in the measured amount of smoke. The machine learning model may be pre-trained based on training data then executed by a smoke detector to accurately distinguish between likely emergencies and nuisance conditions

On-site forest fire smoke detection by low-power autonomous vision sensor

Early detection plays a crucial role to prevent forest fires from spreading. Wireless vision sensor networks deployed throughout high-risk areas can perform fine-grained surveillance and thereby very early detection and precise location of forest fires. One of the fundamental requirements that need to be met at the network nodes is reliable low-power on-site image processing. It greatly simplifies the communication infrastructure of the network as only alarm signals instead of complete images are transmitted, anticipating thus a very competitive cost. As a first approximation to fulfill such a requirement, this paper reports the results achieved from field tests carried out in collaboration with the Andalusian Fire-Fighting Service (INFOCA). Two controlled burns of forest debris were realized (www.youtube.com/user/vmoteProject). Smoke was successfully detected on-site by the EyeRISTM v1.2, a general-purpose autonomous vision system, built by AnaFocus Ltd., in which a vision algorithm was programmed. No false alarm was triggered despite the significant motion other than smoke present in the scene. Finally, as a further step, we describe the preliminary laboratory results obtained from a prototype vision chip which implements, at very low energy cost, some image processing primitives oriented to environmental monitoring.Ministerio de Ciencia e Innovación 2006-TIC-2352, TEC2009-1181

Real Time Video Based Smoke Detection Using Double Optical Flow Estimation

In this paper, we present a video based smoke detection algorithm based on TVL1 optical flow estimation. The main part of the algorithm is an accumulating system for motion angles and upward motion speed of the flow field. We optimized the usage of TVL1 flow estimation for the detection of smoke with very low smoke density. Therefore, we use adapted flow parameters and estimate the flow field on difference images. We show in theory and in evaluation that this improves the performance of smoke detection significantly. We evaluate the smoke algorithm using videos with different smoke densities and different backgrounds. We show that smoke detection is very reliable in varying scenarios. Further we verify that our algorithm is very robust towards crowded scenes disturbance videos