Conference Paper

Fire Smoke Detection in Video Images Using Kalman Filter and Gaussian Mixture Color Model

DOI: 10.1109/AICI.2010.107 Conference: Artificial Intelligence and Computational Intelligence (AICI), 2010 International Conference on, Volume: 1
Source: IEEE Xplore

ABSTRACT Fire smoke detections are crucial for forest resource protections and public security in surveillance systems. A novel approach for smoke detections with combined Kalman filter and a Gaussian color model is proposed in the paper in open areas. Moving objects are firstly generated by image subtractions from adaptive background of a scene through Kalman filter and MHI(Moving History Image) analysis. Then a Gaussian color model, trained from samples offline by an EM algorithm, is performed to detect candidate fire smoke regions. Final validation is carried out by temporal analysis of dynamic features of suspected smoke areas where higher frequency energies in wavelet domains and color blending coefficients are utilized as smoke features. Experimental results show the proposed method is capable of detecting fire smoke reliably.

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    • "Those papers showed that GMM approach was excellent against many environmental conditions. The GMM approaches are applicable for detecting transparent moving such as smoke [4] [5]. Motivation of using GMM as a segmentation method is because GMM are able to detect complex object, and it is robust against light changing and scene changing in a long time. "
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    ABSTRACT: Fire accident remains a problem in modern society. This leads great efforts in finding ways to prevent, detect and control it. Conventional fire detection systems are mostly point detectors, which have limitation for early smoke detection, especially in a high-ceiling atrium. A video-based smoke detection system is an interesting alternative approach. It has better area coverage and detecting smoke faster. In this work, a video-based smoke detection system was developed with two main processes, i.e. moving objects segmentation with Gaussian Mixture Models (GMM) and smoke classifications with Mathematical Model of Meaning (MMM). In the MMM model, the interpretation of dangerous smoke is based on the context provided. Then the classification results are compared with conventional smoke detector. The results show that MMM can recognize the dangerous smoke faster than conventional smoke detectors.
    Procedia Engineering 12/2013; 62:963-971. DOI:10.1016/j.proeng.2013.08.149
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    • "There are many motion detection approaches used in smoke detection systems , such as adaptive background estimation [2, 10–13], block mean difference [14] [15] [16] or motion history image [17] [18]. One parameter is common to all these approaches: the sensitivity of the motion detection. "
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    ABSTRACT: 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.
    Computer Vision and Image Understanding 01/2013; DOI:10.1016/j.cviu.2013.10.003 · 1.54 Impact Factor
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    • "To solve such variation of patterns in the color or shapes, technique such as monitoring and tracking the area of concerned overtime has been suggested. In this case special filters and training method to recognize fire as the object of interest would be applied [16].This process is usually time-consuming and complicated. Then, there is another problem of shadow. "
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    ABSTRACT: Background subtraction is widely used for extracting unusual motion of object of interest in video images. In this paper, we propose a fast and flexible approach of object detection based on an adaptive background subtraction technique that also effectively eliminates shadows based on color constancy principle in RGB color space. This approach can be used for both outdoor and indoor environments. Our proposed method of background subtraction makes use of multiple thresholding technique for detecting object of interests for any given scene. Once the moving object has been detected from the complex background, then the shadows are detected and eliminated by considering some environmental parameters.
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