A　two-level　hierarchical　scheme　for　video-based　person　re-identification　(re-id)　is　presented,　with　the　aim　of　learning　a　pedestrian　appearance　model　through　more　complete　walking　cycle　extraction.　Specifically,　given　a　video　with　consecutive　frames,　the　objective　of　the　first　level　is　to　detect　the　key　frame　with　lightweight　Convolutional　neural　network　(CNN)　of　PCANet　to　reflect　the　summary　of　the　video　content.　At　the　second　level,　on　the　basis　of　the　detected　key　frame,　the　pedestrian　walking　cycle　is　extracted　from　the　long　video　sequence.　Moreover,　local　features　of　Local　maximal　occurrence　(LOMO)　of　the　walking　cycle　are　extracted　to　represent　the　pedestrian＇　s　appearance　information.　In　contrast　to　the　existing　walking-cycle-based　person　re-id　approaches,　the　proposed　scheme　relaxes　the　limit　on　step　number　for　a　walking　cycle,　thus　making　it　flexible　and　less　affected　by　noisy　frames.　Experiments　are　conducted　on　two　benchmark　datasets:　PRID　2011　and　iLIDS-VID.　The　experimental　results　demonstrate　that　our　proposed　scheme　outperforms　the　six　state-of-art　video-based　re-id　methods,　and　is　more　robust　to　the　severe　video　noises　and　variations　in　pose,　lighting,　and　camera　viewpoint.