Person　search　is　a　challenging　computer　vision　task　that　aims　to　simultaneously　locate　and　identify　a　query　person　from　panoramic　images.　To　address　the　issue　of　scene　similarity　and　its　impact　on　search　accuracy　and　efficiency,　we　propose　a　query　based　gallery　selector　module　that　employs　cosine　similarity　to　calculate　the　similarity　between　candidate　images　in　the　gallery　and　the　query　persons　feature　embedding,　then　selects　and　reorders　images　in　the　gallery　based　on　their　similarity　to　the　query　person,　thus　improving　the　accuracy　and　efficiency　of　searching.　Furthermore,　we　introduce　a　mask-aware　mechanism　that　improves　the　localization　loss　function　for　predicted　bounding　boxes.　During　training,　the　network　is　guided　to　increase　its　robustness　in　occluded　scenarios.　Experimental　results　on　public　person　search　datasets　PRW　and　CUHK-SYSU　demonstrate　the　effectiveness　of　our　proposed　method.　©　2024,　The　Author(s),　under　exclusive　license　to　Springer　Nature　Singapore　Pte　Ltd.