A　moving　object　detection　algorithm　with　sparse　motion　field　estimation,　motion　classification　and　pixel-wise　segmentation　is　proposed.　Firstly,　sparse　motion　field　is　recovered　by　fast　corner　detection　and　tracking.　The　corners　that　belong　to　the　same　motion　pattern　are　classified　according　to　their　motion　consistency,　then,　the　resulting　corner　group　is　used　to　reconstructed　scene　image,　and　the　foreground　corners　are　identified　by　getting　rid　of　the　group　with　the　least　reconstruction　error.　Finally,　optimal　dense　segmentation　of　the　foreground　is　performed　by　using　graph　cuts,　the　energy　function　of　which　integrates　corner　motion,　local　color　distribution　and　image　edges.　The　proposed　method　is　tested　on　the　dataset　of　real　complex　scenarios　and　its　effectiveness　is　demonstrated　in　the　results.