多帧背景差与Cauchy模型融合的目标检测  

Target Detection Method Based on Multi-frame Background Subtractionand Cauchy Model

作　　者：王凯[1] 吴敏[1] 姚辉[1] 杨樊[1] 张翔[2] 

机构地区：[1]中国民用航空总局第二研究所科研开发中心,成都610041 [2]电子科技大学电子工程学院,成都611731

出　　处：《光电工程》2016年第10期12-17,共6页Opto-Electronic Engineering

基　　金：国家自然科学基金资助项目(61139003;61179060;U1433112);国家科研院所技术开发研究专项资金资助(2013EG125056)

摘　　要：为有效解决复杂监视场景中快速、准确检测运动目标,提出一种多帧背景差与柯西(Cauchy)模型融合的目标检测方法。该方法首先借鉴Surendra背景模型的思路进行改进,采用多帧背景差法获取干净的背景图像,然后利用实时的视频图像和当前的背景图像进行绝对差分处理,最后通过Cauchy模型对整幅绝对差分图像上的点进行背景点和前景点判别,实现对复杂监视场景中目标的准确检测。针对车辆、行人等不同对象的监控场景下进行实验,验证了本文方法不仅能够有效地抑制噪声及伪目标的干扰,而且能够快速、准确地分割出前景目标。To effectively solve the problem of fast and accurate detection of moving targets in complex surveillance scene, target detection method based on multi-frame background subtraction and Cauchy model is proposed. Firstly, Surendra background model is improved to get clean background image. Then, system judges the current pixel on the absolute differential image belonging to the target areas or background areas by the absolute difference between the current background frame and the real-time video frame. Finally, through the Cauchy distribution model of the pixel, the aim of the moving target detection is realized in complex surveillance scene. The experiment on the vehicle, pedestrian and other object shows that the method can not only suppress the noise and interference of false target, but also can segment foreground target rapidly and accurately.

关 键 词：多帧背景差 Cauchy模型 目标检测 Surendra背景模型 绝对差分图像 

分 类 号：TP317.4[自动化与计算机技术—计算机软件与理论]