递推式视频前景分割方法

本文描述了一种基于前一帧已有分割结果对当前帧进行自动分割的递推式视频前景目标分割方法.目前视频分割主要有以下三大难点尚未被很好地解决,首先是前景与背景的颜色区分度过小,造成不易将其分割.其次,视频中频繁出现的局部遮挡与暴露区域,会破坏递推式视频分割的连续性,使得分割推理错误,造成结果不正确.最后,前/背景颜色模型所选用的采样方法,更是影响结果的重要因素之一.因此本文着重对此三类问题给出了相应的解决方案,利用前景物体具有局部运动一致性的特征,解决前/背景相似颜色问题.对于遮挡与暴露问题,通过自适应的局部窗口调整进行扩展采样,以修复其递推连续性.其次,本文使用了一种基于近似颜色块的采样方法,使得所建立的颜色模型更为明确干净,以减少不相关信息所带来的影响.最后,利用视频的时空一致性特征,合成出最终的分割结果.实验表明,本文所提出的递推式视频分割方法,能有效地解决以上三个问题,并且相比于其他方法,特别地对于复杂的视频测试场景,本文方法能获取更好的自动分割效果.

Recursive video segmentation

This paper presents a kind of recursive video object segmentation method. Previous video cutout methods present three major limitations especially for complex video scene： firstly they lack the ability to deal with the inseparable color problem between foreground and background scenes, and the occlusion/disocclusion problem caused by large movement, new exposed regions or topology change are also difficult to be solved. Lastly, the consideration on how to build the color model for the following probability estimation, which plays a critical role in the final result, has always been ignored in most of existing methods. In our method, all above limitations are taken full consideration. A kind of motion prediction method based on local coherence is adopted to separate the inseparable color. Then a self-adaptive extended sampling method is used to repair the video discontinuity caused by occlusion/disocclusion problem, and also we built the color model by sampling all the pixels from selected regions in order to make it clean and representative. Lastly, the final segmentation result is generated by using 3D Graph Cut algorithm according to the spatio-temporal coherence of the video. The experimental results are presented to demonstrate the effectiveness of the proposed method at achieving high quality results, as well as the robustness of the proposed method against several challenging test inputs.