针对火灾早期预警和危险化学品气体泄漏的在线检测问题,提出了一种基于视频的早期烟雾检测新方法。整个检测过程主要由四部分组成:1)RGB分量运算参数的离线学习;2)用RGB分量运算+位屏蔽,实时分割出类似烟雾区;3)用在背景维护下的减背景来消除场景中静态的类似烟雾区干扰,再由小波变换去除场景中动态的类烟雾区干扰;4)通过长序列的多帧证据积累分析,判别标记出真正烟雾。实验表明,该方法对于320×240分辨率彩色视频的分析判别速度可达16～20fps,并可在3～4s内准确判别出动态场景下的核心烟雾区,且不受大空间、开放环境的限制,具有较强的误报免疫能力。

A novel method to detect early smoke is presented by processing the video for fire-alarming and gas leak-alarming.The whole detection procedure is mainly composed of four parts:1) off-line learning of the operation parameters of RGB color components;2) RGB color components operation combined with bit-masked color reduction to extract the smoke-like regions in real time;3) the background subtraction by background updating to remove those static disturbing regions,and then,the wavelet-analysis after the RGB color components operation to remove those dynamic disturbing regions;4) the multi-frames evidence analysis of long video sequence to check and label out the real smoke,i.e.all of the accumulated clues are combined to reach a final decision.Experimental results show that the proposed method can detect the early smoke correctly under a dynamic complex scene with the processing speed up to 16～20 fps for color image sequence of 320×240 resolutions,and the time needed for the decision-making is within 3～4 s.The novel method is robust to noises and is of high adaptability to monitor large and open spaces under a complex scene.