Those　existing　delayed　association　algorithms　of　non-overlapping　cameras　always　lead　to　target　mismatching　and　subsequence　match　missing.　To　solve　these　two　problems,　this　paper　proposes　pedestrian　instant　alignment　through　discriminative　feature　learning.　First,　this　paper　employs　a　siamese　network　to　integrates　a　pedestrian　verification　model　with　a　pedestrian　identification　one.　The　integrated　model　can　extract　discriminative　appearance　features　from　single　frame　of　targeted　pedestrian　and　calculate　similarity　for　a　pair　of　pedestrians.　Second,　this　paper　presents　an　instant　alignment　model　for　pedestrians　across　non-overlapping　cameras.　The　fundamental　of　proposed　instant　alignment　model　is　minimum　cost　flow　algorithm.　Hence,　according　to　a　match　degree　which　is　associated　with　appearance,　spatial　and　temporal　context,　a　dynamic　minimum　cost　flow　graph　is　established　and　solved　in　real　time.　The　experimental　results　show　that,　on　the　pedestrian　recognition　datasets　Market-1501　and　CUHK03,　the　combination　of　pedestrian　verification　and　identification　model　can　improve　the　efficiency　of　feature　extraction　and　the　generalization　ability　significantly.　The　alignment　performance　of　the　proposed　model　is　superior　to　the　Gate-SCNN　and　S-LSTM.　Furthermore,　on　dataset　NLPR_MCT,　the　benchmark　dataset　for　pedestrian　tracking　of　non-overlapping　cameras,　the　instant　alignment　accuracy　of　the　proposed　model　increases　by　3.3%　compared　to　the　champion　algorithm　in　2014　ECCV　cross-camera　pedestrian　tracking　challenge,　which　is　a　delayed　association　algorithm.　The　experiment　results　also　show　that　the　proposed　model　ranks　second,　just　6.6%　lower　than　the　state-of-the-art　performance.　When　the　propose　model　is　applied　to　inter-cameras　pedestrian　tracking,　the　tracking　accuracy　is　also　higher　than　most　popular　algorithms.　©　2019,　Beijing　China　Science　Journal　Publishing　Co.　Ltd.　All　right　reserved.