In order to improve the accuracy and robustness of occlusion detection under non-rigid motion and large displacements,we propose an occlusion detection method of image sequence motion based on optical flow and multiscale context.First,we design a multiscale context information aggregation network based on dilated convolu-tion which obtains a wider range of image features through multiscale context information of image sequence.Then,we construct an end-to-end motion occlusion detection network model based on multiscale context and optical flow using feature pyramid,utilize the optical flow to optimize the performance of occlusion detection in areas of non-ri-gid motion and large displacements region.Finally,we present a novel motion edge training loss function to obtain the accurate motion occlusion boundary.We compare and analysis our method with the existing representative ap-proaches by using the MPI-Sintel datasets and KITTI datasets,respectively.The experimental results show that the proposed method can effectively improve the accuracy and robustness of motion occlusion detection,especially gains the better occlusion detection robustness under non-rigid motion and large displacements.
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