Traffic scene recognition based on deep cnn and vlad spatial pyramids

TL;DR

This paper proposes a novel traffic scene recognition method combining CNN features with VLAD encoding and spatial pyramids, achieving effective classification on a 10-category dataset.

Contribution

It introduces a new approach that integrates CNN, VLAD encoding, and spatial pyramids for improved traffic scene recognition.

Findings

Achieved satisfactory classification accuracy on a 10-category traffic scene dataset.

Demonstrated the effectiveness of combining CNN features with VLAD and spatial pyramids.

Showed that the proposed method outperforms traditional feature encoding techniques.

Abstract

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.