基于双分数阶变分光流模型的运动目标检测方法

变分光流法是常用的运动目标检测方法,应用场景中的光照变化会极大影响现存变分光流法的稳定性及准确率,提出基于分数阶的变分光流模型来提高光照变化鲁棒性.该模型将分数阶导数同时应用于经典变分光流模型的数据项及平滑项中;根据图像的数据特征,采用图像函数及光流向量函数的有限离散二重分数阶导数近似拟合求导结果计算光流向量,进而将光...

A Motion Detection Method for Dual Fractional-order Variational Optical Flow Model

Motion detection is a basic problem in the field of image processing and optical flow model is one of the frequently used motion detection method.To enhance the robustness of optical flow estimation in illumination changes scene,a dual fractional-order variational optical flow model is proposed.The model is a fractional-order version of HS(Horn&Schunck)model.In particular,the fractional-order derivative is applied in the brightness and smoothing constraint equation of HS model.Two dimensional fractional differential masks are proposed and applied to process the simplification of the model.In order to solve the isotropy of dual fractional differential mask,super-pixel segmentation is applied to adjust the shape of the mask,so as to limit the mask into one super-pixel region with a same or similar motion style.Using fractional-order derivative in smoothness constraint equation makes the model preserving the discontinuity of the optical flow model.Using two-dimensional fractional differential mask,the brightness value and optical flow value of pixels surround target points would be merged into target points,do so the robustness of the model to illumination changes improves.What’s more,two-dimensional fractional differential mask is also applied to calculate dual fractional fractional-order variational optical flow model,do so calculate procedures are simplified and the algorithm is easily programmed.Experiments are done in three widely used public dataset and image sequences extracted from outdoor videos,experiment results show that compared to conventional variational optical flow model,the superiority of our model is the robustness in illumination changes scenes;Compared with other algorithms,the operation speed of the algorithm in this paper is moderate.