计算机科学 ›› 2014, Vol. 41 ›› Issue (12): 251-254.doi: 10.11896/j.issn.1002-137X.2014.12.054

• 图形图像与模式识别 • 上一篇    下一篇

基于颜色直方图和小波变换的视频烟雾检测

孙建坤,杨若瑜   

  1. 南京大学计算机科学与技术系 南京210046;南京大学软件新技术国家重点实验室 南京210046
  • 出版日期:2018-11-14 发布日期:2018-11-14
  • 基金资助:
    本文受国家自然科学基金(60703020,61272218)资助

Smoke Detection in Video Based on Color Histogram and Wavelets

SUN Jian-kun and YANG Ruo-yu   

  • Online:2018-11-14 Published:2018-11-14

摘要: 从视频中自动识别烟雾在火灾早期预报警、尾气识别等实际应用中具有重要意义。烟雾检测的难点之一在于如何排除与烟雾颜色相近的运动对象的干扰。为了保证检测效果和实时性,提出使用静态的小波统计特征来排除汽车、行人等干扰区域。该算法首先使用背景剪除法获取运动区域,然后利用颜色直方图映射来提取符合烟雾色彩特征的疑似区,最后分别对背景和相应的视频帧做小波变换并对二者求差,根据差值图像的统计特征来去除疑似区域中的非烟雾物体。实验结果表明,该方法正确率较高,检错率较低,且基本达到实时效果。

关键词: 烟雾检测,背景减除,直方图反射,小波变换

Abstract: Automatic smoke detection in video plays a major role in the early detection and response of an unexpected fire hazard and the recognition of vehicle exhaust.One of the difficulties in smoke detection is eliminating the interference of moving objects with similar color as smoke effectively.In order to ensure the effectiveness and timeliness,the static statistical character of wavelet transforms was used,which can exclude the interferences such as cars or persons.At first,motion regions are obtained by background subtraction and then candidate smoke regions are extracted through histogram backproject method.At last,the wavelet transform is applied on the background and the corresponding video frame,and the difference between those two transformed images is yielded.According to the different statistical character of smoke and non-smoke,non-smoke is removed from the frame.The experimental results are impressive with limited false alarms,high accuracy and real-time capability.

Key words: Smoke detection,Background subtraction,Histogram backproject,Wavelet transformation

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