In　clinical　operations,　it　is　crucial　for　surgeons　to　know　the　location　of　the　surgical　instrument.　Traditional　positioning　systems　have　difficulty　dealing　with　camera　occlusion,　marker　occlusion,　and　environmental　interference.　To　address　these　issues,　we　propose　a　distributed　visual　positioning　system　for　surgical　instrument　tracking　in　surgery.　First,　we　design　the　marker　pattern　with　a　black　and　white　triangular　grid　and　dot　that　can　be　adapted　to　various　instrument　surfaces　and　improve　the　marker　location　accuracy　of　the　feature.　The　cross-points　in　the　marker　are　the　features　that　each　feature　has　a　unique　ID.　Furthermore,　we　proposed　detection　and　identification　for　the　position-sensing　marker　to　realize　the　accurate　location　and　identification　of　features.　Second,　we　introduce　multi　Perspective-n-Point　(mPnP)　method,　which　fuses　feature　coordinates　from　all　cameras　to　deduce　the　final　result　directly　by　the　intrinsic　and　extrinsic　parameters.　This　method　provides　a　reliable　initial　value　for　the　Bundle　Adjustment　algorithms.　During　instrument　tracking,　we　assess　the　motion　state　of　the　instrument　and　select　either　dynamic　or　static　Kalman　filtering　to　mitigate　any　jitter　in　the　instrument’s　movement.　The　core　algorithms　comparison　experiment　indicates　our　positioning　algorithm　has　a　lower　reprojection　error　comparison　to　the　mainstream　algorithms.　A　series　of　quantitative　experiments　showed　that　the　proposed　system　positioning　error　is　below　0.207 mm,　and　the　run　time　is　below　118.842 ms.　The　results　demonstrate　the　tremendous　clinical　application　potential　of　our　system　providing　accurate　positioning　of　instruments　promoting　the　efficiency　and　safety　of　clinical　surgery.　©　2024,　Australasian　College　of　Physical　Scientists　and　Engineers　in　Medicine.