Different　from　previous　work,　the　study　reported　in　this　paper　attempts　to　simulate　a　more　real　and　complex　approach　for　region　of　interest　(ROI)　detection　and　quantitatively　analyze　the　correlation　between　human　visual　system　(HVS)　and　ROI.　In　this　paper,　an　approach　of　ROI　detection　based　on　visual　attention　and　gaze　tracking　is　proposed.　The　works　include　pre-ROI　estimation　using　visual　attention　model,　gaze　data　collection　and　ROI　detection.　Pre-ROIs　are　segmented　by　the　visual　attention　model.　Since　eye　feature　extraction　is　critical　to　the　accuracy　and　performance　of　gaze　tracking,　adaptive　eye　template　and　neural　network　is　employed　to　predict　gaze　points.　By　computing　the　density　of　the　gaze　points,　ROIs　are　ranked.　Experimental　results　show　that　the　accuracy　of　our　ROI　detection　method　can　be　raised　as　high　as　97%　and　our　approach　can　efficiently　adapt　to　users＇　interests　and　match　the　objective　ROI.　©　2012　IEEE.