  Traffic scene recognition is an important and challenging issue in
Intelligent Transportation Systems (ITS). Recently, Convolutional Neural
Network (CNN) models have achieved great success in many applications,
including scene classification. The remarkable representational learning
capability of CNN remains to be further explored for solving real-world
problems. Vector of Locally Aggregated Descriptors (VLAD) encoding has also
proved to be a powerful method in catching global contextual information. In
this paper, we attempted to solve the traffic scene recognition problem by
combining the features representational capabilities of CNN with the VLAD
encoding scheme. More specifically, the CNN features of image patches generated
by a region proposal algorithm are encoded by applying VLAD, which subsequently
represent an image in a compact representation. To catch the spatial
information, spatial pyramids are exploited to encode CNN features. We
experimented with a dataset of 10 categories of traffic scenes, with
satisfactory categorization performances.
