Moving　object　detection　under　a　dynamic　background　has　been　a　serious　challenge　in　real-time　computer　vision　applications.　Global　motion　compensation　approaches,　a　popular　existing　technique,　aims　at　compensating　the　moving　background　for　moving　target　segmentation.　However,　it　suffers　from　inaccurate　global　motion　parameters　estimation.　The　paper　presents　a　moving　object　detection　technique　that　combines　TV-L1　optical　flow　with　SLIC　superpixel　segmentation　to　characterize　moving　objects　from　a　dynamic　background.　SLIC　superpixel　segmentation　can　adhere　to　boundaries　of　objects,　and　thus　improve　the　segmentation　performance.　TV-L1　optical　flow　implemented　on　GPU　reports　competitive　smooth　flow　field　with　real-time　performance.　Experimental　results　on　various　challenging　sequences　demonstrate　that　the　proposed　approach　achieve　impressive　performance.