Visual　object　tracking　is　a　fundamental　research　topic　in　computer　vision.　In　this　paper,　we　proposed　a　novel　hybrid　tracking　method　based　on　Pulse　Coupled　Neural　Network　(PCNN)　and　Multiple　Instance　Learning　(MIL).　Most　modern　trackers　may　be　inaccurate　when　the　training　samples　are　imprecise　which　causes　drift.　To　resolve　these　problems,　MIL　method　is　introduced　into　the　tracking　task,　which　can　alleviate　drift　to　some　extent.　However,　the　MIL　tracker　may　detect　the　positive　sample　that　is　less　important.　PCNN　is　different　from　traditional　artificial　neural　networks,　which　can　be　applied　in　many　image　processing　fields,　such　as　image　segmentation.　So,　the　PCNN　was　employed　as　sample　detector　which　can　know　the　most　important　sample　when　training　the　classifier.　Then,　a　more　robust　and　much　faster　tracker　is　proposed　to　approximately　maximize　the　bag　likelihood　function.　Empirical　results　on　a　large　set　of　sequences　demonstrate　the　superior　performance　of　the　proposed　approach　in　robustness,　stability　and　efficiency　to　state-of-the-art　methods　in　the　literature.