Wavelet-based smoke detection in outdoor video sequences. Gonzalez-Gonzalez, R., Alarcon-Aquino, V., Rosas-Romero, R., Starostenko, O., Rodriguez-Asomoza, J., & Ramirez-Cortes, J., M. In 2010 53rd IEEE International Midwest Symposium on Circuits and Systems, pages 383-387, 8, 2010. IEEE. ![Wavelet-based smoke detection in outdoor video sequences [link]](https://bibbase.org/img/filetypes/link.svg "link")
Website doi abstract bibtex In this paper an approach to detect smoke columns from outdoor forest video sequences is proposed. The approach follows three basic steps. The first step is an image pre-processing block which resizes the image by applying a bicubic interpolation algorithm. The image is then transformed to its intensity values with a gray-scale transformation and finally the image is grouped by common areas with an image indexation. The second step consists of a smoke detection algorithm which performs a stationary wavelet transform (SWT) to remove high frequencies on horizontal, vertical, and diagonal details. The inverse SWT is then implemented and finally the image is compared to a non-smoke scene in order to determine the possible regions of interest (ROI). In order to reduce the number of false alarms, the final step of the proposed approach consists on a smoke verification algorithm, which determines whether the ROI is increasing its area or not. These results are combined to reach a final decision for detecting a smoke column on a sequence of static images from an outdoor video. Experimental results show that multi-resolution wavelet analysis is more accurate than the traditional low-pass filters on this application. © 2010 IEEE.
@inproceedings{
title = {Wavelet-based smoke detection in outdoor video sequences},
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abstract = {In this paper an approach to detect smoke columns from outdoor forest video sequences is proposed. The approach follows three basic steps. The first step is an image pre-processing block which resizes the image by applying a bicubic interpolation algorithm. The image is then transformed to its intensity values with a gray-scale transformation and finally the image is grouped by common areas with an image indexation. The second step consists of a smoke detection algorithm which performs a stationary wavelet transform (SWT) to remove high frequencies on horizontal, vertical, and diagonal details. The inverse SWT is then implemented and finally the image is compared to a non-smoke scene in order to determine the possible regions of interest (ROI). In order to reduce the number of false alarms, the final step of the proposed approach consists on a smoke verification algorithm, which determines whether the ROI is increasing its area or not. These results are combined to reach a final decision for detecting a smoke column on a sequence of static images from an outdoor video. Experimental results show that multi-resolution wavelet analysis is more accurate than the traditional low-pass filters on this application. © 2010 IEEE.},
bibtype = {inproceedings},
author = {Gonzalez-Gonzalez, R. and Alarcon-Aquino, V. and Rosas-Romero, R. and Starostenko, O. and Rodriguez-Asomoza, J. and Ramirez-Cortes, J. M.},
doi = {10.1109/MWSCAS.2010.5548865},
booktitle = {2010 53rd IEEE International Midwest Symposium on Circuits and Systems}
}
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