This　paper　addresses　the　issue　of　frontal　object　perception　in　real-world　traffic　scenarios.　Accurate　and　real-time　frontal　object　perception　plays　a　key　role　in　Advanced　Driver　Assistance　Systems　(ADAS)　and　Intelligent　Vehicles　(IV).　However,　perceiving　complex　traffic　environments,　which　contain　multiple　classes　of　on-road　objects　with　various　visual　appearances　from　different　viewpoints　and　partial　observations,　is　still　a　challenging　task.　In　this　paper,　a　perception　system　fusing　a　millimeter-wave　(MMW)　radar　and　a　monocular　camera　is　proposed.　Firstly,　the　detections　of　MMW　radar　are　converted　to　regions　of　interest　(ROIs)　on　the　image.　Then,　these　ROIs　are　classified　by　four　classifiers　using　Deformable　Part　Model　(DPM).　Finally,　a　mixer　module　is　used　to　combine　all　the　classification　results　and　infer　the　final　result　for　each　ROI.　The　computation　intensity　of　the　DPM　algorithm　can　be　efficiently　reduced　through　this　mechanism.　Meanwhile,　high　detection　precision　is　achievable.　Experiment　results　show　that　the　proposed　frontal　object　perception　system　can　detect　and　classify　on-road　objects　in　complex　urban　traffic　scenarios　with　98.4%　detection　rate　at　nearly　real-time　performance　(29Hz).