中文摘要：

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。

英文摘要：

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.