Automatic Early Forest Fire Detection Based on Gaussian Mixture Model

2018 ◽  
Author(s):  
Jiye Qian ◽  
Jin Fu ◽  
Jide Qian ◽  
Weibin Yang ◽  
Ke Wang ◽  
...  
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Forest Fire ◽  
Fire Detection ◽  
Gaussian Mixture ◽  
Forest Fire Detection

scholarly journals Video Summarization Based on Gaussian Mixture Model and Kernel Support Vector Machine for Forest Fire Detection

2019 ◽  
Vol 9 (1) ◽  
pp. 1827-1831
Keyword(s):  
Support Vector Machine ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Forest Fire ◽  
Video Summarization ◽  
Fire Detection ◽  
Gaussian Mixture ◽  
Support Vector ◽  
Kernel Support Vector Machine ◽  
Forest Fire Detection

Exponential growth in the generation of multimedia data especially videos resulted to the development of video summarization concept. The summary of the videos offers a collection of frames which precisely define the video content in a considerably compacted form. Video summarization models find its applicability in various domains especially surveillance. This paper intends to develop a video summarization technique for the application of forest fire detection. The proposed method involves a set of processes namely convert frames, key frame extraction, feature extraction and classification. Here, a Merged Gaussian Mixture Model (MGMM) is applied for the process of extracting key frames and kernel support vector machine (KSVM) is employed for classifying a frame into normal frame and forest fire frame. The simulation analysis is performed on the forest fire video files from FIRESENSE database and the results are assessed under several dimensions. The final outcome proves the efficiency of the presented MGMM-KSVM model in a considerable way.

Video fire detection based on Gaussian Mixture Model and multi-color features

2017 ◽  
Vol 11 (8) ◽  
pp. 1419-1425 ◽  
Author(s):  
Xian-Feng Han ◽  
Jesse S. Jin ◽  
Ming-Jie Wang ◽  
Wei Jiang ◽  
Lei Gao ◽  
...  
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Fire Detection ◽  
Gaussian Mixture ◽  
Color Features

scholarly journals SVM based forest fire detection using static and dynamic features

2011 ◽  
Vol 8 (3) ◽  
pp. 821-841 ◽  
Author(s):  
Jianhui Zhao ◽  
Zhong Zhang ◽  
Shizhong Han ◽  
Chengzhang Qu ◽  
Zhiyong Yuan ◽  
...  
Keyword(s):  
Forest Fire ◽  
Bright Light ◽  
Fire Detection ◽  
Gaussian Mixture ◽  
Detection Algorithm ◽  
Svm Classifier ◽  
Final Decision ◽  
Dynamic Features ◽  
Novel Approach ◽  
Forest Fire Detection

A novel approach is proposed in this paper for automatic forest fire detection from video. Based on 3D point cloud of the collected sample fire pixels, Gaussian mixture model is built and helps segment some possible flame regions in single image. Then the new specific flame pattern is defined for forest, and three types of fire colors are labeled accordingly. With 11 static features including color distributions, texture parameters and shape roundness, the static SVM classifier is trained and filters the segmented results. Using defined overlapping degree and varying degree, the remained candidate regions are matched among consecutive frames. Subsequently the variations of color, texture, roundness, area, contour are computed, then the average and the mean square deviation of them are obtained. Together with the flickering frequency from temporal wavelet based Fourier descriptors analysis of flame contour, 27 dynamic features are used to train the dynamic SVM classifier, which is applied for final decision. Our approach has been tested with dozens of video clips, and it can detect forest fire while recognize the fire like objects, such as red house, bright light and flying flag. Except for the acceptable accuracy, our detection algorithm performs in real time, which proves its value for computer vision based forest fire surveillance.

Automated Deep Learning based Video Summarization Approach for Forest Fire Detection

2021 ◽  
pp. 54-61
Author(s):  
Ahmed N. Al Al-Masri ◽  
◽  
◽  
Ahmed N. Al Al-Masri
Keyword(s):  
Deep Learning ◽  
Forest Fire ◽  
Experimental Validation ◽  
Short Term Memory ◽  
Video Summarization ◽  
Fire Detection ◽  
Gaussian Mixture ◽  
Video Data ◽  
Validation Process ◽  
Forest Fire Detection

Due to the exponential increase in video data, an automated examination of videos has become essential. A significant requirement is the capability of the automated video summarization process, which is helpful in vast application areas from surveillance to security. It assists in monitoring the user application with reduced memory and time. Therefore, this paper designs an automated deep learning-based video summarization approach for forest fire detection (ADLVS-FFD). The ADLVS-FFD technique aims to summarize the captured videos and detects the existence of forest fire in it. In addition, the ADLVS-FFD technique involves different subprocesses such as frame splitting, feature extraction, and classification. Besides, a merged Gaussian mixture model (MGMM) is used to extract keyframes and features. Moreover, the long short-term memory (LSTM) model is employed to detect and classify input images into normal and forest fire images. To ensure the better performance of the ADLVS-FFD technique, a comprehensive experimental validation process takes place on a benchmark video dataset. The resultant experimental validation process highlighted the supremacy of the ADLVS-FFD technique over the recent methods.

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