An efficient traffic sign recognition based on graph embedding features

• Published in: Neural computing & applications Vol. 31; no. 2; pp. 395 - 407
• Main Authors: Gudigar, Anjan, Chokkadi, Shreesha, Raghavendra, U., Acharya, U. Rajendra
• Format: Journal Article
• Language: English
• Published: London Springer London 14.02.2019; Springer Nature B.V
• 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; Computer vision; Feature selection; GIST; Traffic sign recognition; Intelligent transportation system; Real-world driver assistance system
• ISSN: 0941-0643; 1433-3058
• DOI: 10.1007/s00521-017-3063-z

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.