一种用于智能监控的目标检测和跟踪方法

在对现有目标检测、跟踪算法进行分析对比的基础上，设计并实现了一种简单有效的目标检测和跟踪方案。首先提出了一种基于像素灰度归类和单模态高斯模型的背景重构算法，能够利用多帧包含前景目标的场景图像重构准确的背景模型。进而以此为基础采用背景减法进行各帧中目标的检测，并选取形心作为匹配特征实现了场景中多个目标的有效跟踪。实验表明，该方法实现简单，无须事先提供背景图像即可实现目标的准确检测和跟踪，其性能明显优于传统基于时间平均背景模型的方法。

Object detection and tracking method for intelligent surveillance

This paper designed and realized a simple and effective object detection and tracking scheme,based on a review of the existed detection and tracking algorithms.At first,proposed a pixel intensity classification and the single Gaussian model based background reconstruction algorithm,which could provide an accurate background model through a sequence of scene images with foreground objects.Then used the background subtraction method for object detection,selected the center of the object as the matching feature for tracking of multi-objects among the sequence.Experimental results show that the proposed algorithm and scheme is simple to realize,and can detect and track the moving objects effectively,it shows obvious perfor-mance improvement compared with the traditional time-averaged background based method.