Featured　Application　The　vehicle　detection　algorithm　proposed　in　this　work　could　be　used　in　autonomous　driving　systems　to　understand　the　environment,　or　could　be　applied　in　surveillance　systems　to　extract　useful　transportation　information　through　a　camera.　Vehicle　detection　as　a　special　case　of　object　detection　has　practical　meaning　but　faces　challenges,　such　as　the　difficulty　of　detecting　vehicles　of　various　orientations,　the　serious　influence　from　occlusion,　the　clutter　of　background,　etc.　In　addition,　existing　effective　approaches,　like　deep-learning-based　ones,　demand　a　large　amount　of　training　time　and　data,　which　causes　trouble　for　their　application.　In　this　work,　we　propose　a　dictionary-learning-based　vehicle　detection　approach　which　explicitly　addresses　these　problems.　Specifically,　an　ensemble　of　sparse-and-dense　dictionaries　(ESDD)　are　learned　through　supervised　low-rank　decomposition;　each　pair　of　sparse-and-dense　dictionaries　(SDD)　in　the　ensemble　is　trained　to　represent　either　a　subcategory　of　vehicle　(corresponding　to　certain　orientation　range　or　occlusion　level)　or　a　subcategory　of　background　(corresponding　to　a　cluster　of　background　patterns)　and　only　gives　good　reconstructions　to　samples　of　the　corresponding　subcategory,　making　the　ESDD　capable　of　classifying　vehicles　from　background　even　though　they　exhibit　various　appearances.　We　further　organize　ESDD　into　a　two-level　cascade　(CESDD)　to　perform　coarse-to-fine　two-stage　classification　for　better　performance　and　computation　reduction.　The　CESDD　is　then　coupled　with　a　downstream　AdaBoost　process　to　generate　robust　classifications.　The　proposed　CESDD　model　is　used　as　a　window　classifier　in　a　sliding-window　scan　process　over　image　pyramids　to　produce　multi-scale　detections,　and　an　adapted　mean-shift-like　non-maximum　suppression　process　is　adopted　to　remove　duplicate　detections.　Our　CESDD　vehicle　detection　approach　is　evaluated　on　KITTI　dataset　and　compared　with　other　strong　counterparts;　the　experimental　results　exhibit　the　effectiveness　of　CESDD-based　classification　and　detection,　and　the　training　of　CESDD　only　demands　small　amount　of　time　and　data.