Optical flow field is an important basis for many computer vision tasks such as target detection and unmanned aerial vehicle positioning.In order to develop the accuracy and robustness of optical flow estimation suffered from the difficult motion such as non-rigid movement and large displacement motion

this paper proposes a large displacement optical flow estimation approach based on non-rigid dense patch matching.Firstly

we utilize the non-rigid dense patch matching to compute the initial nearest neighbor field between the consecutive frames

and eliminate the inconsistent regions of the computed nearest neighbor field according to the consistency of the neighboring patches in the image to obtain an accurate image nearest neighbor field.Secondly

we merge the nearest neighbor field into the TV-L1(Total Variational with L1 norm

TV-L1) optical flow model

and employ the nearest neighbor field to compensate the large displacement optical flow of TV-L1 model by using the quadratic pseudo-Boolean optimization (QPBO) fusion algorithm during the coarse-to-fine computation scheme.Finally

we employ the standard test image sequences to evaluate the performance of our approach and some state-of-the-art methods including LDOF(Large Displacement Optical Flow

LDOF)

Classic+NL

NNF(Nearest Neighbor Fields

NNF) and FlowNet2.0.The experimental results demonstrate that the proposed method has the higher accuracy and better robustness of optical flow estimation for difficult motion such as non-rigid movement

large displacement motion and motion occlusion.