Vehicle　color　recognition　is　easily　affected　by　subtle　environmental　changes.　The　existing　recognition　methods　cannot　achieve　an　accurate　result.　A　high-accuracy　vehicle　color　recognition　method　using　a　hierarchical　fine-tuning　strategy　for　urban　surveillance　videos　is　proposed.　Different　from　the　conventional　convolutional　neural　networks-based　methods,　which　usually　obtain　a　single　classification　model,　the　proposed　method　combines　pretraining　and　hierarchical　fine-tunings　to　obtain　different　classification　models　that　can　adapt　to　the　change　of　illumination　conditions.　First,　the　GoogLeNet　is　pretrained　using　the　ILSVRC-2012　dataset　to　obtain　the　initial　weight　parameters　of　the　network.　During　the　first　stage　of　fine-tuning,　the　whole　vehicle　color　dataset　is　used　to　fine-tune　the　pretrained　results　to　get　the　initial　classification　model.　Then,　an　image　quality　assessment　method　is　proposed　to　evaluate　the　illumination　conditions　of　the　image.　The　whole　vehicle　color　dataset　is　divided　into　some　subdatasets　according　to　the　evaluation　results.　The　second　stage　of　fine-tuning　is　performed　on　the　initial　classification　model　using　each　subdataset.　Thus,　the　final　classification　models　for　the　subdatasets　are　obtained.　The　experimental　results　on　different　databases　demonstrate　that　the　recognition　accuracy　of　the　proposed　method　can　achieve　superior　performance　over　the　state-of-the-art　methods.　(C)　2018　SPIE　and　IS&T