Target　tracking　is　an　important　application　of　unmanned　aerial　vehicles　(UAVs).　The　template　is　the　identity　of　the　target　and　has　a　great　impact　on　the　performance　of　target　tracking.　Most　methods　only　keep　the　latest　template　of　the　target,　which　is　intuitive　and　convenient　but　has　poor　ability　to　resist　the　change　of　target　appearance,　especially　to　reidentify　a　target　that　has　disappeared　for　a　long　time.　In　this　paper,　we　propose　a　practical　multiobject　tracking　(MOT)　method,　which　uses　historical　information　of　targets　for　better　adapting　to　appearance　variations　during　tracking.　To　preserve　the　spatial-temporal　information　of　the　target,　we　introduce　a　memory　pool　to　store　masked　feature　maps　at　different　moments,　and　precise　masks　are　generated　by　a　segmentation　network.　Meanwhile,　we　fuse　the　feature　maps　at　different　moments　by　calculating　the　pixel-level　similarity　between　the　current　feature　map　and　the　masked　historical　feature　maps.　Benefiting　from　the　powerful　segmentation　features　and　the　utilization　of　historical　information,　our　method　can　generate　more　accurate　bounding　boxes　of　the　targets.　Extensive　experiments　and　comparisons　with　many　trackers　on　MOTS,　MOT17,　and　MOT20　demonstrate　that　our　method　is　competitive.　The　ablation　study　showed　that　the　introduction　of　memory　improves　the　multiobject　tracking　accuracy　(MOTA)　by　2.1.