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Automatic segmentation of moving objects considering single moving camera

机译:考虑单个移动摄像机的运动对象自动分割

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In this paper, we present a new method for detecting and accurately extracting the moving object from a video sequence taken by single moving camera. Extracting high-quality dynamic foreground is a challenging problem due to the coupling of color, motion, and occlusion. Many previous approaches assume that the camera motion is restricted. In our method, we relax these restrictions and present a comprehensive system for accurately computing camera motion, background, and foreground object. Based on the robust motion estimation, we are capable of handling challenging videos where the camera contains unknown motions. The accuracy of estimated camera-motion parameters is increased by direct estimation such that the consistent background is achieved. The foreground separation which includes contextual information decreases the number classification errors, greatly improves the foreground separation even on the object boundary. Experimental results of high quality segmentation of moving objects from challenging videos demonstrate the effectiveness of our method.
机译:在本文中,我们提出了一种从单个移动摄像机拍摄的视频序列中检测并准确提取运动对象的新方法。由于颜色,运动和遮挡的耦合,提取高质量的动态前景是一个具有挑战性的问题。先前的许多方法都假定摄像机的运动受到限制。在我们的方法中,我们放宽了这些限制,并提出了一个用于精确计算摄像机运动,背景和前景对象的综合系统。基于强大的运动估计,我们能够处理摄像机包含未知运动的具有挑战性的视频。通过直接估计可以提高估计的摄像机运动参数的准确性,从而获得一致的背景。包含上下文信息的前景分离减少了数量分类错误,甚至在对象边界上也大大提高了前景分离。从具有挑战性的视频中对运动对象进行高质量分割的实验结果证明了我们方法的有效性。

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