The　task　of　person　re-identification　(re-ID)　is　to　find　the　same　pedestrian　across　non-overlapping　camera　views.　Generally,　the　performance　of　person　re-ID　can　be　affected　by　background　clutter.　However,　existing　segmentation　algorithms　cannot　obtain　perfect　foreground　masks　to　cover　the　background　information　clearly.　In　addition,　if　the　background　is　completely　removed,　some　discriminative　ID-related　cues　(i.e.,　back-pack　or　companion)　may　be　lost.　In　this　article,　we　design　a　dual-stream　network　consisting　of　a　Provider　Stream　(P-Stream)　and　a　Receiver　Stream　(R-Stream).　The　R-Stream　performs　an　a　priori　optimization　operation　on　foreground　information.　The　P-Stream　acts　as　a　pusher　to　guide　the　R-Stream　to　concentrate　on　foreground　information　and　some　useful　ID-related　cues　in　the　background.　The　proposed　dual-stream　network　can　make　full　use　of　the　a　priori　optimization　and　guided-learning　strategy　to　learn　encouraging　foreground　information　and　some　useful　ID-related　information　in　the　background.　Our　method　achieves　Rank-1　accuracy　of　95.4%　on　Market-1501,　89.0%　on　DukeMTMC-reID,　78.9%　on　CUHK03　(labeled),　and　75.4%　on　CUHK03　(detected),　outperforming　state-of-the-art　methods.