具有高区分度的视频火焰检测方法

为了在视频监控系统中准确地判断火焰区域并预测火灾的发生,提出一种新的基于人工神经网络的视频火焰检测方法.该方法在分析火焰的运动和三维颜色特征的基础上,分别通过傅里叶变换和圆形度分析、角点检测的方法研究火焰的闪烁频率、几何形状对应的时空域特征,采用获得的各类特征构成概率向量作为人工神经网络分类模型的输入,输出表示火灾发生的概率.在保持检测准确率的同时,该方法通过实验选择最优的参数组合解决神经网络容易陷入局部极值及收敛慢的问题.该方法可以区分大空间（隧道、仓库、博物馆等建筑物）中闪烁的车灯和真实火焰,能够避免在实际的视频监控系统应用中将闪烁车灯误判为火焰,有效减少环境光对检测结果的影响,降低火灾火焰的误报率.实验结果表明,采用该方法在保持检测实时性的同时,能够达到96%的检测正确率.

Distinguishable method for video fire detection

A novel method for video fire detection based on artificial neural network was proposed in order to estimate fire regions and predict occurrence of fire in video surveillance system.Temporal-spatial features were analyzed including flicker frequency and geometry of fire by Fourier transformation,roundness degree analysis and corner detection based on motion and three-dimensional color features.Then these features were used as a probability vector fed for artificial neural network classification model to output the probability of fire.With keeping accurate detection rate,two problems,local extremum tendency and slow convergence rate,of artificial neural network were solved by optimal parameters combination experimentally.The method can distinguish flickering vehicle light from actual fire in varied locations,e.g.tunnel,warehouse and museum,to prevent ambiguity and eliminate the influence environment illumination imposing onto the detection results which significantly reduce false positive rate.Experimental results show that the method has a 96% detection rate while keeping a real-time performance.