Optical flow estimation is a significant research in the areas of computer vision

image processing

and pattern recognition.After the original works of Horn and Schunck

as well as those of Lucas and Kanade

the accuracy of flow-field computation has significantly increased via numerous remarkable contributions in the last three decades.However

the robustness of the optical flow estimation is still a challenging task at this stage of development.A non-local total variation with L norm (TV-L) optical flow computational model based on the weighted neighboring triangle filtering has been proposed in this study for the accuracy and robustness of the optical flow estimation under difficult scenes

such as non-rigid motion

motion occlusion and discontinuity

large displacement

and complex edges. First

the nonquadratic penalty function based on the L norm and the combination of the brightness constancy assumption and gradient constancy assumption are employed to constitute the robust data term.Thus

the negative influences of brightness changes

image noise

and motion occlusion and discontinuity can be reduced.Secondly

the nonquadratic penalty function based on the L norm and the image gradient-based self-adaptive weight are introduced to produce the image-and flow-driven smoothing term

thereby addressing the problems of boundary blur and edge over-segmentation caused by non-rigid motion and complex edges.Third

the optical flow computation energy function based on the TV-L model is presented with the proposed robust data term and the smoothing term incorporated with the image and flow information.Finally

the non-local TV-L model for optical flow estimation is proposed by adding the weighted neighboring triangle filtering based on non-local term to the presented classical TV-L energy function to remove the outliers in the estimated flow field caused by the large displacement.The non-local term is replaced using a weighted neighboring triangle-based median filtering to acquire the linearized numerical computational scheme corresponding to the non-local TV-L energy function.The median filtering optimizes the flow field at each layer of the image pyramid through the coarse-to-fine warping strategy. The test sequences of the MPI Sintel and Middlebury databases are employed to evaluate the accuracy and robustness of the proposed method and the other state-of-the-art methods

including large displacement optical flow (LDOF)

total variation regularization of local-global optical flow (CLG-TV)

sparse occlusion detection with optical flow (SOF)

and classic model with non-local constraint (Classic+NL) to illustrate the performance of the proposed method when dealing with non-rigid motion

motion occlusion and discontinuity

large displacement

complex edges

and other challenges.Experimental results show that in comparison with the other state-of-the-art methods

the error statistics indexes of average angle error (AAE) and average endpoint error (AEE) of the proposed method decreased by 47.76% and 89.04% for the MPI test sequences

and decreased by 28.45% and 28.42% for the Middlebury test sequences

respectively.Furthermore

the time consumption of the proposed method increased by 5.16% for the Middlebury test sequences compared to the classical median filtering based method；the added running time of the proposed method may be caused by the image triangulating. The comparison results among the proposed method and other state-of-the-art optical flow computation methods using the MPI Sintel and Middlebury test sequences showed that the proposed method can be better applied to difficult scenes

such as non-rigid motion

motion occlusion and discontinuity

large displacement

and complex edges.This result indicates that the proposed method has higher accuracy and better robustness than the other state-of-the-art methods

especially for the challenges of difficult scenes.