The　traditional　histogram　distribution　based　target　color　model　easily　fails　to　discern　colors　within　a　color　interval　that　cannot　be　too　small　due　to　the　real-time　tracking　requirement.　Moreover,　the　model　is　prone　to　background　interference.　In　this　paper,　a　new　target　color　distribution　model　with　background　suppression　is　proposed　and　synthetic　target　tracking　algorithm　based　on　the　new　model　is　presented.　The　new　model　takes　the　first-　and　second-order　statistical　information　into　account　and　makes　use　of　human　visual　characteristics　for　weight　computation.　This　approach　allows　to　distinguish　different　colors　within　the　same　color　interval　and　to　suppress　the　proportion　of　background　colors　in　the　target　model.　The　proposed　algorithm　builds　up　a　target　generative　model　on　the　basis　of　the　new　color　model　and　figures　out　a　target　shape　discriminative　model　using　the　correlation　filter　in　terms　of　histogram　of　oriented　gradient　(HOG)　features.　These　two　models　are　then　fused　for　target　tracking.　A　set　of　qualitative　principles　for　setting　fusion　parameters　is　given　for　evaluating　the　individual　confidence　of　both　models　and　for　model　updating.　Finally,　a　particle　swarm　optimization　algorithm　is　applied　to　identify　the　locations　and　scales　of　candidate　targets　using　a　fitness　function　determined　by　the　tracking　result　of　the　fusion　model.　The　experimental　results　show　that　the　proposed　algorithm　in　most　cases　outperforms　the　other　algorithms　in　terms　of　accuracy　while　meeting　the　requirement　of　real　time　tracking.　Copyright　©　2021　Acta　Automatica　Sinica.　All　rights　reserved.