An　optical　flow　method　combining　Gaussian　convoluted　data　term　with　non-local　median　filter　is　proposed　to　remove　noise　and　consequently　improve　the　robustness　and　accuracy　of　the　optical　flow　estimation.　Robust　L1　norm　is　employed　for　construction　of　data　term,　which　is　smoothed　with　Gaussian　filter　to　suppress　noise,　and　primal-dual　method　is　introduced　to　improve　the　estimation　efficiency　of　variational　optical　flow.　A　global　optimization　strategy　based　on　non-local　median　filter　is　used　to　further　enhance　the　estimation　accuracy.　The　coarse-to-fine　pyramid　technique　is　employed　to　improve　the　adaptability　of　the　algorithm　for　large　displacements　estimation.　The　proposed　method　is　evaluated　by　using　both　the　Middlebury　optical　flow　database　images　and　real　scene　images.　The　experimental　results　show　that　the　proposed　method　performs　good　robustness　and　accuracy　in　contrast　with　traditional　TV-L1　model　algorithms.