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A Multi-Video Vehicle Tracking Method Based on Camshift with Color Interference

机译:一种基于CLOWS干扰的多视频车辆跟踪方法

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To avoid the situation that the Camshift algorithm which based on color characteristic will lose the target vehicle when other similar-color vehicles appear in the same video, we propose a method that can apply to multi-video tracking. Firstly, set Bhattacharyya distance at 0.8 as the standard of a successful tracking. Then in continuous frame image, use the Kalman filtering to estimate the next position of the target in order to narrow the tracking range and avoid losing target. In the case of discontinuous frame image, we suggest that use the color characteristic of the camshaft to track at first. If tracking fails due to color interference, then use the SIFT to track targets. Based on the above, we can achieve multi-video tracking. The experiment shows the method has the accuracy rate of 94.8% in continuous frame image and 91.4% in discontinuous frame image under a poor environment excluding a poor visibility.
机译:为避免基于颜色特性的CAMShift算法将在其他类似的彩色车辆出现在同一视频中时将丢失目标车辆,我们提出了一种可以应用于多视频跟踪的方法。首先,将Bhattacharyya距离为0.8作为成功跟踪的标准。然后在连续帧图像中,使用卡尔曼滤波来估计目标的下一个位置,以缩小跟踪范围并避免失去目标。在不连续帧图像的情况下,我们建议使用凸轮轴的颜色特性首先跟踪。如果由于颜色干扰而导致跟踪失败,则使用SIFT进行跟踪目标。基于以上,我们可以实现多视频跟踪。实验表明,该方法在连续帧图像中的精度率​​为94.8%,在不包括差的环境下的不连续框架图像中的91.4%。

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