Authors:
Vartika Sengar
1
;
Renu M. Rameshan
1
and
Senthil Ponkumar
2
Affiliations:
1
School of Computing and Engineering, Indian Institute of Technology, Mandi, Himachal Pradesh, India
;
2
Continental Automotive Components Pvt. Ltd., Bengaluru, Karnataka, India, India
Keyword(s):
Hierarchical Classification, Spectral Clustering, Convolutional Neural Networks, Machine Learning, Data Processing, Image Processing, Pattern Recognition.
Abstract:
Traffic Sign Recognition is very crucial for self-driving cars and Advanced Driver Assistance Systems. As the vehicle moves within a region or across regions, it encounters a variety of signs which needs to be recognized with very high accuracy. It is generally observed that traffic signs have large intra-class variability and small inter-class variability. This makes visual distinguishability between distinct classes extremely irregular. In this paper we propose a hierarchical classifier in which the number of coarse classes is automatically determined. This gives the advantage of dedicated classifiers trained for classes which are more difficult to distinguish. This is an application oriented work which involves systematic and intelligent combination of machine learning and computer vision based algorithms with required modifications for designing fully automated hierarchical classification framework for traffic sign recognition. The proposed solution is a real-time scalable machin
e learning based approach which can efficiently take care of wide intra-class variations without extracting desired handcrafted features beforehand. It eliminates the need for manually observing and grouping relevant features, thereby reducing human time and efforts. The classifier performance accuracy is surpassing the accuracy achieved by humans on publicly available GTSRB traffic sign dataset with lesser parameters than the existing solutions.
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