In most of the graph-based segmentation methods, prior saliency regions are often obtained by analyzing motion and appearance information and then the energy model was minimized for further segmentation. These methods often ignore refined analysis of appearance information, and are not robust to complex scenarios. Since information entropy can measure sample purity and information entropy minimization has a consistent goal with energy model minimization, a video object segmentation via information entropy constraint was proposed. Firstly, the segmentation results of the first stage were obtained by combining with optical flow vector and the point-in-polygon principle from the computational geometry. Secondly, the uniform movement and performance were gained through presenting superpixel as the basic division unit. Finally, video segmentation was formulated as a pixel labeling optimization problem with two labels by introducing information entropy constraint into energy function, and more accurate segmentation results were obtained by minimizing the energy function. The experimental results on public datasets show that the proposed method can effectively improve the robustness of video object segmentation.