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Traffic scene recognition is an important and challenging issue inIntelligent Transportation Systems (ITS). Recently, Convolutional NeuralNetwork (CNN) models have achieved great success in many applications,including scene classification. The remarkable representational learningcapability of CNN remains to be further explored for solving real-worldproblems. Vector of Locally Aggregated Descriptors (VLAD) encoding has alsoproved to be a powerful method in catching global contextual information. Inthis paper, we attempted to solve the traffic scene recognition problem bycombining the features representational capabilities of CNN with the VLADencoding scheme. More specifically, the CNN features of image patches generatedby a region proposal algorithm are encoded by applying VLAD, which subsequentlyrepresent an image in a compact representation. To catch the spatialinformation, spatial pyramids are exploited to encode CNN features. Weexperimented with a dataset of 10 categories of traffic scenes, withsatisfactory categorization performances.

Traffic scene recognition based on deep cnn and vlad spatial pyramids