Background　subtraction　is　commonly　employed　in　foreground　object　detection　in　urban　traffic　scenes.　Most　of　the　current　color　or　texture　feature-based　background　subtraction　models　are　easily　contaminated　by　sudden　and　gradual　illumination　variations　in　urban　traffic　scenes.　To　resolve　this　deficiency,　an　adaptive　local　median　texture　feature,　which　extracts　the　adaptive　distance　threshold　employing　the　median　information　in　a　predefined　local　region　of　a　pixel　and　Weber＇s　law,　is　introduced.　In　addition,　a　sample　consensus-based　model　that　evolved　from　portable　visual　background　extractor　is　proposed　using　an　adaptive　local　median　texture　feature.　Then,　the　foreground　is　labeled　by　comparing　the　input　video　frames　feature　with　the　model.　Moreover,　to　adapt　the　dynamic　background,　the　random　update　scheme　is　used　to　update　the　model.　Extensive　experimental　results　on　the　public　Change　Detection　data　set　of　2014　(CDnet2014)　and　the　real-world　urban　traffic　videos　demonstrate　that　our　background　subtraction　method　is　superior　to　the　other　state-of-the-art　texture-feature-based　methods.　The　qualitative　and　quantitative　results　show　the　encouraging　efficiency　of　the　proposed　technique　to　deal　with　sudden　and　gradual　illumination　variations　in　real-world　urban　traffic　scenes.