A Method of Smoke Detection Based on Various Features Combination
Abstract: Smoke-like regions greatly increase smoke detection errors in the video. In order to improve the accuracy of smoke detection, a smoke detection method based on BP neural network is proposed, combining the wavelet feature, smoke texture feature and mean of Y component pixel value. Firstly, moving regions in the video sequences are ex- tracted; secondly, the wavelet feature and texture feature of suspected regions are extracted, then a new kind of multi-feature vector is formed. Finally, feature vector is input into the BP neural network classifier for smoke detection. The experiments show that smoke detection results are more effective by combing various features.
文章引用: 郑雪梅 , 杨 胜 (2013) 一种基于多种特征组合的烟雾检测方法。 计算机科学与应用， 3， 239-243. doi: 10.12677/CSA.2013.35041
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