We propose a new method to extract foreground edges in a video streams taken from a stationary camera. Our background model is based on the fact that a background pixel's gradient components follow Gaussian mixture model(GMM). GMM is performed on the initial group of video frames to obtain the initial pixel gradient component distribution information at each pixel. Then each of the current Canny edge pixels is classified into foreground or background pixel based on its gradient components' weighted square sum of distances from their respective mean values. If the difference is larger than a threshold, it is then classified as a foreground pixel, otherwise a background pixel in which case the GMM information is accordingly updated. If the ratio of the number of foreground pixels over the total number of Canny edge pixel is large than a certain threshold, a new GMM background modeling is trigger. The algorithm is implemented in Visual C++ and tested on a laptop powered by an Intel Pentium 3.0GHz. The experiment shows the algorithm is highly selective in extracting valid foreground edge pixels and it's speed is 43 ms/frame for a video stream of 640×480 and shows that the method is applicable for real-time processing.
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机译：我们提出了一种新方法，提取从固定相机拍摄的视频流中的前景边。我们的背景模型基于背景像素的梯度组件遵循高斯混合模型（GMM）。在初始视频帧组上执行GMM以在每个像素处获得初始像素梯度分量分布信息。然后，每个当前的罐头边缘像素基于其各自平均值的梯度分量的加权平方和距离基于前景或背景像素。如果差异大于阈值，则将其归类为前景像素，否则是背景像素，在这种情况下，在这种情况下相应地更新了GMM信息。如果在Canny Edge像素总数上的前景像素数量的比率大于某个阈值，则新的GMM背景建模是触发器。该算法在Visual C ++中实现，并在由英特尔Pentium 3.0GHz供电的笔记本电脑上进行测试。实验表明该算法在提取有效的前景边缘像素方面具有高度选择性，并且它的速度为640×480的视频流的43毫秒/帧，并表明该方法适用于实时处理。
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