针对当前光流算法在光照不足的环境下不能进行弱纹理区域的运动目标分割问 题，提出了一种自适应双分数阶光流( ADFOVOF) 模型． 该模型在双分数阶光流( DFOVOF) 模型的基础上，通过图像信噪比计算 DFOVOF 模型中数据项的分数阶微分掩模的 阶次及尺寸; 应用以光流向量为特征的超像素调整 DFOVOF 模型中平滑项的分数阶微分 掩模的形状． 实验结果表明，在光照不足或者光照变化剧烈的场景下，文中算法能够精确 估计光流场，获得清晰的运动目标轮廓． 

An adaptive dual fractional order variational optical flow ( ADFOVOF) model was proposed to solve the problem that the current optical flow algorithm cannot segment moving objects in weak texture region under insufficient illumination． The model was an adaption version of dual fractional order optical flow ( DFOVOF) model that adopts fractional differential mask in both，the data term and smoothness term of traditional Horn ＆ Schunck ( HS) model． Specially，the order and size of fractional order differential mask for each region were adjusted by image signal to noise ratio ( SNＲ) whereas，the shape of the fractional order differential mask was regulated by super-pixel that prevents the mask to interfere by surrounding regions． The experiment results show that our algorithm can get accurate optical flow field and obtain clear motion outlines under insufficient illumination or illumination changes scenes．