As　an　instance-level　recognition　problem,　the　key　to　effective　vehicle　re-identification　(Re-ID)　is　to　carefully　reason　the　discriminative　and　viewpoint-invariant　features　of　vehicle　parts　at　high-level　and　low-level　semantics.　However,　learning　part-based　features　requires　a　laborious　human　annotation　of　some　factors　as　attributes.　To　address　this　issue,　we　propose　a　region-aware　multi-resolution　(RAMR)　Re-ID　framework　that　can　extract　features　from　a　series　of　local　regions　without　extra　manual　annotations.　Technically,　the　proposed　method　improves　the　discriminative　ability　of　the　local　features　through　parallel　high-to-low　resolution　convolutions.　We　also　introduce　a　position　attention　module　to　focus　on　the　prominent　regions　that　can　provide　effective　information.　Given　that　the　vehicle　Re-ID　performance　can　be　affected　by　background　clutters,　we　use　the　image　obtained　through　foreground　segmentation　to　extract　local　features.　Results　show　that　using　original　and　foreground　images　can　enhance　the　Re-ID　performance　compared　with　using　either　the　original　or　foreground　images　alone.　In　other　words,　the　original　and　foreground　images　complement　each　other　in　the　vehicle　Re-ID　process.　Finally,　we　aggregate　the　global　appearance　and　local　features　to　improve　the　system　performance.　Extensive　experiments　on　two　publicly　available　vehicle　Re-ID　datasets,　namely,　VeRi-776　and　VehiclelD,　are　conducted　to　validate　the　effectiveness　of　each　proposed　strategy.　The　findings　indicate　that　the　RAMR　model　achieves　significant　improvement　in　comparison　with　other　state-of-the-art　methods.　(C)　2020　SPIE　and　IS&T