An efficient traffic sign recognition based on graph embedding features

Traffic sign recognition (TSR) is one of the significant modules of an intelligent transportation system. It instantly assists the drivers to efficiently recognize the traffic sign. Recognition of traffic sign is a large-scale feature learning problem with different real-world appearances. The main...

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Bibliographic Details
Published in:Neural computing & applications 2019-02, Vol.31 (2), p.395-407
Main Authors: Gudigar, Anjan, Chokkadi, Shreesha, Raghavendra, U., Acharya, U. Rajendra
Format: Article
Language:English
Subjects:
Artificial Intelligence
Benchmarks
Classifiers
Computational Biology/Bioinformatics
Computational Science and Engineering
Computer Science
Computing time
Data Mining and Knowledge Discovery
Discriminant analysis
Feature recognition
Image Processing and Computer Vision
Modules
Neural networks
Original Article
Personal computers
Probability and Statistics in Computer Science
Support vector machines
Traffic control
Traffic signs
Transportation systems
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Summary:Traffic sign recognition (TSR) is one of the significant modules of an intelligent transportation system. It instantly assists the drivers to efficiently recognize the traffic sign. Recognition of traffic sign is a large-scale feature learning problem with different real-world appearances. The main goal of this paper is to develop an efficient TSR method, which can run on an ordinary personal computer (PC). In the proposed method, GIST descriptors of the traffic sign images are extracted and subjected to graph-based linear discriminant analysis to reduce the dimension. Moreover, it effectively learns the discriminative subspace through the graph structure with increased computational efficiency. An efficient TSR module is built by conducting series of experiments using support vector machine, extreme learning machine, and k -nearest neighbor ( k -NN) classifiers on available public datasets. Our approach achieved the highest recognition accuracy of 96.33 and 97.79% using k -NN classifier for German Traffic Sign Recognition Benchmark (GTSRB) and Belgium Traffic Sign Classification Benchmark (BelgiumTSC), respectively. Also it achieved 99.1% accuracy for a subcategory of GTSRB traffic signs and able to predict the class of unknown traffic sign within 0.0019 s on an ordinary PC. Hence, it can be used in real-world driver assistance system.
ISSN:0941-0643
1433-3058
DOI:10.1007/s00521-017-3063-z