Fire Smoke Detection in Video Images Using Kalman Filter and Gaussian Mixture Color Model
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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Conference Proceeding: Smoke Detection in Video.[show abstract] [hide abstract]
ABSTRACT: In this paper, we propose a method for smoke detection in outdoor video sequences. We assume that the camera is mounted on a pan/tilt device. The proposed method is composed of three steps. The first step is to decide whether the camera is moving or not. While the camera is moving, we skip the ensuing steps. Otherwise, the second step is to detect the areas of change in the current input frame against the background image and to locate regions of interest(ROIs) by connected component analysis. The block-based approach is applied in both the first and second steps. In the final step, we decide whether the detected ROI is smoke by using the k- temporal information of its color and shape extracted from the ROI. We show the experimental results using in the forest surveillance videos.CSIE 2009, 2009 WRI World Congress on Computer Science and Information Engineering, March 31 - April 2, 2009, Los Angeles, California, USA, 7 Volumes; 01/2009
Conference Proceeding: Smoke detection in open areas using its texture features and time series properties[show abstract] [hide abstract]
ABSTRACT: In extensive facilities such as port facilities, chemical plants, and power stations, it is important to detect a fire early and certainly. The purpose of this paper is to present a new smoke detection method in open areas, as smoke is considered as a significant signal of the fire. It is assumed that the camera monitoring the scene of the open area is stationary. Since smoke does not keep stationary shape or image features like edges, it is difficult apply ordinal image processing techniques such as the edge or contour detection directly. In this paper, we propose a novel method of the smoke detection in an image sequence, in which we combines the several images techniques to detect smoke. We apply it to images of open areas under general environmental conditions. First, moving objects are detected from gray.scale image sequences, and then the noise is removed with the image binarization and the morphological operation. Furthermore, since the smoke pattern must be examined, the smoke feature is extracted with the texture analysis. Then, to obtain the final result of the proposed method, we discussed the properties of the proposed features as the time series data.Industrial Electronics, 2009. ISIE 2009. IEEE International Symposium on; 08/2009
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ABSTRACT: This paper proposes a novel method to detect smoke in video. It is assumed the camera monitoring the scene is stationary. The smoke is semi-transparent at the early stages of a fire. Therefore edges present in image frames start loosing their sharpness and this leads to a decrease in the high frequency content of the image. The background of the scene is esti-mated and decrease of high frequency energy of the scene is monitored using the spatial wavelet transforms of the cur-rent and the background images. Edges of the scene pro-duce local extrema in the wavelet domain and a decrease in the energy content of these edges is an important indicator of smoke in the viewing range of the camera. Moreover, scene becomes grayish when there is smoke and this leads to a decrease in chrominance values of pixels. Periodic be-havior in smoke boundaries is also analyzed using a Hidden Markov model (HMM) mimicking the temporal behavior of the smoke. In addition, boundary of smoke regions are rep-resented in wavelet domain and high frequency nature of the boundaries of smoke regions is also used as a clue to model the smoke flicker. All these clues are combined to reach a final decision.