The　video　based　detection　of　fire　hazards　is　one　of　the　significant　developments　in　recent　years.　However,　the　conventional　vision　based　flame　detection　algorithms　suffer　with　the　issues　of　low　detection　rate　and　high　false-alarm　rate.　A　novel　real　time　video　processing　method　is　proposed　in　this　paper　that　detects　the　flames　by　combining　the　flame　motion　detection　technique　with　color　clues　and　flame　flicker　to　reach　the　final　detection.　The　proposed　fire　detection　framework　builds　an　efficient　background　model　by　optimizing　the　selective　background　update　to　extract　the　fire-like　moving　regions　in　the　video　frames.　Furthermore,　a　YCbCr　color　space　based　analysis　technique　is　applied　to　improve　the　fire-pixel　classification.　Finally,　a　flame　flicker　identification　algorithm　based　on　the　statistical　frequencies　is　used　to　confirm　whether　it　is　fire　region.　The　experimental　results　show　that　the　proposed　algorithm　has　high　detection　rate　and　low　false-alarm　rate,　it　is　accurate,　robust　and　effective　compared　to　the　existing　methods.　Copyright　©　2015　Binary　Information　Press.