Visual　target　tracking　is　an　important　function　in　real-time　video　monitoring　application,　whose　performance　determines　the　implementation　of　many　advanced　tasks.　At　present,　Siamese-network　trackers　based　on　template　matching　show　great　potential.　It　has　the　advantage　of　balance　between　accuracy　and　speed,　due　to　the　pre-trained　convolutional　network　to　extract　deep　features　for　target　representation　and　off-line　tracking　of　each　frame.　During　tracking,　however,　the　target　template　feature　is　only　obtained　from　the　first　frame　of　the　video　in　the　existing　algorithms.　The　tracking　performance　is　completely　depending　on　the　framework　of　template　matching,　resulting　in　the　independence　of　frames　and　ignoring　the　feature　of　inter-frame　connection　of　video　sequence.　Therefore,　the　existing　algorithms　do　not　perform　well　in　the　face　of　large　deformation　and　severe　occlusion.　We　propose　a　long　short-term　memory　(LSTM)　improved　Siamese　network　(LSiam)　model,　which　takes　advantages　of　both　time-domain　regression　capability　of　the　LSTM　and　the　balanced　ability　in　tracking　accuracy　and　speed　of　Siamese　network.　It　focus　on　the　temporal　and　spatial　correlation　information　between　video　sequences　to　improve　the　traditional　Siamese-network　trackers　with　an　LSTM　prediction　module.　In　addition,　an　improved　template　updating　module　is　constructed　to　combine　the　original　template　with　the　changed　appearance.　The　proposed　model　is　verified　in　two　types　of　difficult　scenarios:　deformation　challenge　and　occlusion　challenge.　Experimental　results　show　that　our　proposed　approach　can　get　better　performance　in　terms　of　tracking　accuracy.　?　2021　SPIE　and　IS&T