基于轮廓特征及扩展Kalman滤波的车辆跟踪方法研究

在车辆实时跟踪中，基于Kalman滤波的方法是常用的有效方法，但因车辆检测时常将靠近的物体检测成一个目标引起误检现象，这会使在目标匹配时产生错误。为此，首先考察运动区域的长宽比和占空比，进行误检判断；然后提出了一种基于轮廓特征拐点的车辆分割方法；最后引入基于扩展Kalman滤波的跟踪模型。实验结果表明，采用的误检判断准则对多车辆的检测区域有较高的识别率，提出的基于轮廓特征拐点的车辆分割方法可实现重叠遮挡车辆的准确完整分割，用基于扩展Kalman滤波的跟踪模型实现了车辆的实时跟踪。

Tracking method based on contour feature of vehicles and extended Kalman filter

For real-time tracking of moving vehicles, the general and efficient method is based on Kalman filter. However, the false detection often exists when more than one objects approaches with each other. It causes error in target matching process. To overcome the above problem, this paper first considers the width/height ratio and occupancy ratio to make false detection judgment. Then a new moving vehicle segmentation algorithm based on feature points on contour is presented. Lastly, the tracking model based on extended Kalman filter is implemented. Experimental results demonstrate that the rule can recognize false detection quite accurately. The proposed vehicles segmentation method can segment the overlapped ones accurately and completely, and finally the tracking model based on extended Kalman filter is implemented to realize the real-time tracking.