Person　re-identification　aims　to　associate　the　same　person　across　different　views　and　can　be　taken　as　a　subproblem　of　image　retrieval.　It　has　extensive　application　prospects　in　many　areas　such　as　intelligent　video　surveillance,　security,　and　criminal　investigation.　Due　to　poor　illumination　condition,　image　resolution,　camera　viewpoint,　environment,　and　pedestrian　pose,　person　re-identification　has　become　one　of　the　challenging　problems　in　computer　vision.　Early　person　re-identification　methods　mostly　rely　on　hand-crafted　features　and　researches　are　conducted　on　small-scale　datasets.　In　recent　years,　the　emergence　of　large-scale　datasets　and　rapid　development　of　deep　learning　techniques　provide　person　re-identification　with　new　opportunities.　This　survey　gives　a　detailed　overview　of　the　history,　state　of　the　art,　and　typical　methods　in　this　domain.　Firstly,　the　general　framework　of　person　re-identification　is　presented.　Then,　feature　representation,　similarity　measurement,　and　two　key　aspects　of　person　re-identification,　are　further　summarized,　respectively.　We　also　highlight　the　application　of　rapid　developing　deep　learning　techniques　to　person　re-identification.　Moreover,　the　representative　datasets　of　person　re-identification　and　methods　of　obtaining　excellent　performance　on　each　dataset　are　analyzed　and　compared.　Finally,　the　future　trends　of　this　field　are　discussed.　Copyright　©　2018　Acta　Automatica　Sinica.　All　rights　reserved.