目的 针对非刚性运动、运动遮挡与间断、大位移以及复杂边缘结构等困难场景图像序列光流计算的准确性与鲁棒性问题，提出一种基于加权邻域三角滤波的非局部TV-L¹光流计算方法。方法 首先设计非平方惩罚函数L¹模型与梯度守恒假设相结合的数据项，然后引入基于L¹模型与基于图像梯度自适应变化权重相结合的平滑项，并根据提出的鲁棒数据项与图像-光流联合控制平滑项建立TV-L¹光流计算能量函数模型。最后采用基于加权邻域三角的非局部约束项，通过引入图像金字塔分层变形计算策略，在每层图像光流计算时对光流计算结果进行基于加权邻域三角网格的中值滤波优化，提出基于加权邻域三角滤波的非局部TV-L¹光流计算模型。结果 分别采用MPI与Middlebury数据库测试图像序列对本文方法和LDOF、CLG-TV、SOF、Classic+NL等代表方法进行实验对比。本文方法光流计算结果的平均角误差（AAE）和平均端点误差（AEE）相对其他对比方法平均下降28.45%和28.42%，时间消耗相对传统方法增长5.16%。结论 相对于传统的光流计算方法，本文方法针对非刚体运动、运动遮挡与间断、大位移运动以及复杂边缘等困难场景具有较好的适用性，光流估计结果具有较高的精度和较好的鲁棒性。

Objective 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 L¹ norm (TV-L¹) 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.Method First, the nonquadratic penalty function based on the L¹ 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 L¹ 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-L¹ 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-L¹ model for optical flow estimation is proposed by adding the weighted neighboring triangle filtering based on non-local term to the presented classical TV-L¹ 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-L¹ energy function.The median filtering optimizes the flow field at each layer of the image pyramid through the coarse-to-fine warping strategy.Result 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.Conclusion 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.