Due to the clutter background motion, accurate moving object segmentation in unconstrained videos remains a significant open problem, especially for the slow-moving object. This article proposes an accurate moving object segmentation method based on robust seed selection. The seed pixels of the object and background are selected robustly by using the optical flow cues. Firstly, this article detects the moving object’s rough contour according to the local difference in the weighted orientation cues of the optical flow. Then, the detected rough contour is used to guide the object and the background seed pixel selection. The object seed pixels in the previous frame are propagated to the current frame according to the optical flow to improve the robustness of the seed selection. Finally, we adopt the random walker algorithm to segment the moving object accurately according to the selected seed pixels. Experiments on publicly available data sets indicate that the proposed method shows excellent performance in segmenting moving objects accurately in unconstraint videos.

中文翻译：

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基于鲁棒种子像素选择的无约束视频中的精确运动对象分割

由于杂乱的背景运动，无约束视频中的准确运动对象分割仍然是一个重大的开放问题，尤其是对于缓慢移动的对象。本文提出了一种基于鲁棒种子选择的精确运动对象分割方法。对象和背景的种子像素是通过使用光流线索来鲁棒地选择的。首先，本文根据光流加权方向线索的局部差异检测运动物体的粗略轮廓。然后，检测到的粗略轮廓用于指导对象和背景种子像素的选择。根据光流将前一帧中的对象种子像素传播到当前帧，以提高种子选择的鲁棒性。最后，我们采用随机游走算法根据选择的种子像素对运动物体进行精确分割。在公开可用数据集上的实验表明，所提出的方法在无约束视频中准确分割运动对象方面表现出优异的性能。