Object　proposals　has　become　the　preprocessing　in　many　vision　pipelines　especially　in　object　localization　systems.　While　with　the　development　and　widespread　of　proposal　localization,　it　is　hard　to　decide　which　method　is　more　suitable　for　object　detection.　In　this　paper　we　provide　detailed　understanding　about　differences　of　five　object　proposal　methods.　And　we　provide　a　set　of　evaluation　metrics　and　use　these　metrics　to　compare　and　evaluate　the　recall　situation　of　these　object　proposal　methods.　Using　the　ground-truth　instance　for　exploring　unsupervised　object　localization　on　PASCAL　VOC　2007,　MSRA10K　and　Object　Discovery　datasets.　Our　experimental　analysis　demonstrates　that　the　performance　of　these　methods　depends　on　the　number　of　candidates　and　the　threshold　of　recall,　meanwhile　the　methods　that　generate　the　candidates　with　scores　perform　better　than　those　methods　that　do　not　with　scores.　Our　findings　show　the　advantages　and　disadvantages　of　these　methods,　and　provide　insights　and　metrics　to　motivate　the　evaluation　of　object　proposal　methods.