The　problem　of　object　contour　tracking　in　image　sequences　remains　challenging,　especially　those　with　cluttered　backgrounds.　In　this　paper,　the　fast　two-cycle　level　set　method　with　narrow　perception　of　background　(FTCNB)　is　proposed　to　extract　the　foreground　objects,　e.g.　vehicles　from　road　image　sequences.　The　curve　evolution　of　the　level　set　method　is　implemented　by　computing　the　signs　of　region　competition　terms　on　two　linked　lists　of　contour　pixels　rather　than　by　solving　partial　differential　equations　(PDEs).　The　curve　evolution　process　mainly　consists　of　two　cycles:　one　cycle　for　contour　pixel　evolution　and　a　second　cycle　for　contour　pixel　smoothness.　Based　on　the　curve　evolution　process,　we　introduce　two　tracking　stages　for　the　FTCNB　method.　For　coarse　tracking　stage,　the　speed　function　is　defined　by　region　competition　term　combining　color　and　texture　features.　For　contour　refinement　stage　which　requires　higher　tracking　accuracy,　the　likelihood　models　of　the　Maximum　a　posterior　(MAP)　expressions　are　incorporated　for　the　speed　function.　Both　the　tracking　and　refinement　stages　utilize　the　fast　two-cycle　curve　evolution　process　with　the　narrow　perception　of　background　regions.　With　these　definitions,　we　conduct　extensive　experiments　and　comparisons　for　the　proposed　method.　The　comparisons　with　other　baseline　methods　well　demonstrate　the　effectiveness　of　our　work.　(C)　2016　Elsevier　B.V.　All　rights　reserved.