基于Mean Shift的多视频车辆跟踪研究

为了提高多视频下目标车辆跟踪的准确率,提出了一种基于Mean Shift结合视觉词袋的车辆跟踪方法。该方法采用Mean Shift提供的轮廓和颜色信息进行初匹配,并进行跟踪；针对车辆在不同视频下车辆视角、环境不同的情况,提出了尺度不变的识别方法,即利用视觉词袋特征作为车辆特征进行再次匹配。该方法能够利用高速路网中摄像机拍摄的视频确定目标车辆的具体位置。实验表明,基于Mean Shift的多视频车辆跟踪方法能够有效提高车辆跟踪的准确率。

Research on Multi Video Vehicle Tracking Based on Mean Shift

In order to improve the accuracy of target tracking in multi video,a vehicle tracking method based on Mean Shift combined with visual words was proposed.The method uses Mean Shift to provide the contour and the color information to carry on the initial match and the track.A scale invariant identification method was proposed for the situation of vehicle viewing angle and environment in different video,which regards the visual word bag feature as the vehicle features for match again.The method can be used to determine the specific location of the target vehicle by using the video camera in the high-speed network.Experimental results show that the Shift Mean based multi video vehicle tracking method can improve the accuracy of vehicle tracking.