Video-based Fire and Smoke Detection System

• Main Authors: Chi-Hung Chung, 張吉弘
• Other Authors: Sheng-Wen Shih
• Format: Others
• Language: zh-TW
• Published: 2014
• Online Access: http://ndltd.ncl.edu.tw/handle/06965426742936856902

碩士 === 國立暨南國際大學 === 資訊工程學系 === 102 === Along with the progress of computer technology, sophisticated image processing/understanding methods have developed and the application of intelligent video surveillance system are becoming more and more popular. In this thesis, we use image processing techniques to analyze image features of flame and smoke. The image features are then used to develop a video-based fire and smoke detection system. The proposed system consists of the fire detection module and the smoke detection module. In the fire detection module, we first detect foreground objects with a proper background model. Then, three pre-trained fire color look up tables, an LDA model, the standard deviation of the G-channel, an evaluated flame risk value are used to detect flame in video. In the smoke detection module, we use dark channel analysis to extract suspicious blurry regions from video. Also, we use wavelet analysis to determine whether the high frequency image energy is reducing. Then, smoke candidate regions are computed and are tracked to examine if the area of any of them keeps growing. When the area of a smoke candidate is increasing, it is determined to be a smoke region. Experimental results show that, when the input video resolution is 640×480, the fire and smoke detection speed is 100 frames/sec., and the recognition accuracy is about 92%.