光学学报, 2018, 38 (7): 0710001, 网络出版: 2018-09-05
基于ViBe与机器学习的早期火灾检测算法 下载: 1037次
Early Fire Detection Algorithm Based on ViBe and Machine Learning
图像处理 ViBe 机器学习 火灾检测 前景提取 特征提取 image processing ViBe machine learning fire detection foreground extraction feature extraction
摘要
针对现有视频图像火灾检测算法前景信息丢失严重、误报率高、泛化能力弱等问题,提出一种新的火灾检测算法。其主要由前景提取和分类决策两大模块组成。在前景提取模块中改进ViBe算法,实现对运动区域的选择性更新;同时使用随机森林和支持向量机组成的两级分类器对运动区域颜色进行分类,以获取精确的前景区域。在分类决策模块中,提出两种新的早期火焰特征用于描述帧间火焰区域重叠率和火焰区域不同部分运动剧烈程度比率,再结合Hu矩特征训练出决策分类器。实验结果表明,该算法具有准确率高、误报率低、泛化能力强、响应时间短等优点,并能很好地应用于实际环境中。
Abstract
A new fire detection algorithm is proposed for solving the problems of the existing video image fire detection algorithm, such as serious loss of foreground information, high false alarm rate and weak generalization ability. It mainly consist of two parts including foreground extraction and classification decision. In order to extract more accurate foreground region, an improved ViBe algorithm is applied to obtain the selectively updated motion area. Meanwhile the color features in the motion area are classified with a two-stage classifier composed of random forest and support vector machine. In the classification decision module, two novel kinds of early flame features are suggested to describe the ratio of the inter-frame area overlap rate to the intensity of different sections movement in the flame region, and then combined with the Hu moment feature for training the decision classifier. The experimental results show that the algorithm is more adaptable for practical applications with high accuracy, low false alarm rate, strong generalization ability and short response time.
梅建军, 张为. 基于ViBe与机器学习的早期火灾检测算法[J]. 光学学报, 2018, 38(7): 0710001. Jianjun Mei, Wei Zhang. Early Fire Detection Algorithm Based on ViBe and Machine Learning[J]. Acta Optica Sinica, 2018, 38(7): 0710001.