The　technology　of　near-duplicate　video　detection　is　currently　a　research　hot　spot　in　the　field　of　multimedia　information　processing.　It　has　great　value　in　the　areas　such　as　large　scale　video　information　indexing　and　copyright　protection.　In　the　case　of　large-scale　data,　it　is　very　important　to　ensure　the　accuracy　of　detection　and　robustness,　in　the　meanwhile　improving　the　processing　speed　of　video　copy　detection.　In　this　respect,　a　HVS(Human　Visual　System)-based　video　copy　detection　system　is　proposed　in　this　paper.　This　system　utilizes　the　visual　attention　model　to　extract　the　region　of　interest(ROI)　in　keyframes,　which　extracts　the　Surfgram　feature　only　from　the　information　in　ROI,　rather　than　all　of　the　information　in　the　keyframe,　thus　effectively　reducing　the　amount　of　the　data　to　process.　The　experimental　results　have　shown　that　the　proposed　algorithm　can　effectively　improve　the　speed　of　detection　and　perform　good　robustness　against　brightness　changes,　contrast　changes,　frame　drops　and　Gaussian　noise.