Video object segmentation with shortest path

Unsupervised video object segmentation is to automatically segment the foreground object in the video without any prior knowledge. This paper proposes an object-level method to segment foreground object, while existing methods are normally based on low level information. We firstly find all the object-like regions. Then based on the corresponding map between the successive frames, the video segmentation problem is converted to graph model one. Rather than adopting TRW-S which might result in a local optimal solution, a shortest path algorithm is explored to get a globally optimum solution. Compared with the state-of-the-art object-level method, our method not only guarantees the continuity of segmentation result but also works well even under the big disturbance of fast motion object in the background. The experimental results on two open datasets (SegTrack and Berkeley Motion Segmentation Dataset) and video sequences captured by ourselves demonstrate the effectiveness of our method.