Recognition of Traffic Sign Based on Support Vector Machine and Creation of the Indian Traffic Sign Recognition Benchmark
Traffic sign recognition, a driver assistance system informs and warns the driver about the status of the road is a challenging issue. Though, a lot of work on this topic has been carried out, but complete benchmark datasets are not freely available for comparison of different approaches. A few databases are available for benchmarking automatic detection of traffic signs. However, there is no database built considering the Indian traffic signs. The road scenarios in India are quite different from other countries, especially in rural areas. Hence, an effort to build an Indian traffic sign database considering both rural and urban situations is presented in the work. The database consists of 13000 traffic sign images of 50 different classes of traffic signs taken at different times under different environmental conditions and includes the detailed annotation of the traffic signs in terms of size, type, orientation, illumination and occlusion. The work also discusses an efficient method for identification of road signs based on two modules: (1) feature extraction based on dense scale invariant feature transform (DSIFT) and (2) a classifier trained by support vector machine (SVM). The SIFT approach transforms an image it into a large collection of local feature vectors invariant to scaling, translation or rotation of the image, and reduction in the dimensionality is achieved by applying principal component analysis (PCA). After extracting the features, the image is classified using support vector machine, a supervised learning model.
KeywordsDense scale invariant feature transform Pattern recognition Principal component analysis Support vector machine
This work was carried under Research Promotion Scheme grant from All India Council for Technical Education (AICTE), project Ref. No: 8023/RID/RPS-114(Pvt)/2011–12. Authors wish to thank AICTE, New Delhi.
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