In　recent　years,　object　re-identification　has　been　a　significant　topic　in　computer　vision,　which　is　expected　in　the　application　of　artificial　intelligent　surveillance.　To　tackle　the　problem,　metric　learning　has　become　an　optimal　method,　and　the　approaches　are　concerned　with　learning　a　distance　function　tuned　to　a　particular　identification　task,　and　have　been　shown　to　be　useful　in　conjunction　with　nearest-neighbor　methods　and　other　techniques.　This　paper　provides　a　survey　of　existing　metric　learning　approaches　for　object　re-identification,　which　focuses　on　the　methods　based　on　the　application　of　metric　learning.　The　detailed　description　of　methods　is　presented　for　comparison,　and　several　related　main　datasets　are　briefly　introduced.　In　this　paper,　all　papers　are　published　ranging　from　2013　to　2016,　which　provides　an　overview　of　progress　in　this　area.