To　solve　the　invalid　tracking　of　a　human　target　caused　by　appearance　variations　due　to　large　angle　change　of　the　target　in　a　robot　mobile　tracking,　a　multi-template　regression　weighted　mean-shift　algorithm　was　proposed.　The　algorithm　could　implement　the　target　tracking　by　building　a　multi-template　model　of　the　target　and　applying　mean　shift.　Firstly,　the　template　set　was　obtained　according　to　the　results　from　mean　shift　procedure　of　the　last　frame　and　the　coarse　location　information　of　head-shoulder　model　of　a　current　frame,　by　which　the　position　and　angle　variation　of　the　target　person　were　included.　Then,　the　multi-template　regression　weighted　mean-shift　algorithm　was　used　to　determine　the　precise　location　of　the　target　person.　The　regression　model　was　introduced　to　multi-template　mean　shift　to　implement　a　map　from　color-texture　feature　to　the　similarity　of　target　model　to　limit　the　number　of　templates　and　to　ensure　the　real-time　performance　of　the　target　detection.　Finally,　the　proposed　algorithm　was　verified　by　videos　and　robot　tracking　tests.　The　results　show　that　the　image　average　treatment　time　is　86.4　ms/frame,　which　satisfies　the　requirement　of　person　tracking　for　a　mobile　robot.　The　method　solves　the　appearance　variation　problem　of　targets　in　tracking　processing　and　improves　the　robustness　of　human　targets　to　its　feature　variations.　©　2016,　Science　Press.　All　right　reserved.