The　mean-shift　algorithm　is　generally　used　in　tracking　system,　which　is　based　on　non-parametric　density　estimation　method　and　produces　less　computational　loads.　The　traditional　mean-shift　algorithm　cannot　effectively　handle　inadequate　representation　of　target＇s　color　distribution,　invariable　target　model　and　occlusion.　Aiming　to　resolve　these　problems,　an　adaptive　multi-feature　mean-shift　algorithm　is　proposed.　In　this　algorithm,　an　improved　kernel　function　is　obtained　by　combining　the　silhouette　of　the　target.　This　method　could　eliminate　the　influence　of　the　background,　various　illuminations　and　other　related　limitations.　Furthermore,　the　color-texture　histogram　is　extracted　to　achieve　target　model　effectively.　A　selective　update　strategy　is　proposed　to　adaptively　update　the　target　model.　The　update　scheme　could　not　only　eliminate　the　accumulated　error,　but　also　avoid　drifting　away.　At　last,　extend　Kalman　filter　(EKF)　is　used　to　estimate　the　person＇s　motion　between　consecutive　frames.　Compared　with　the　traditional　algorithm,　the　presented　algorithm　could　track　the　target　successfully　when　the　background　has　similar　color　and　texture.　In　addition,　the　average　tracking　time　is　140　ms/frame,　which　satisfied　the　requirements　of　real-time　target　tracking.　Experimental　results　show　that　the　proposed　algorithm　is　robust　against　similarity　distraction　and　occlusion.