Digital Image Processing Technique for Smart Traffic Management

Digital Image Processing Technique for Smart Traffic Management

Praveen Kumar¹, Nadeem Ahmed²

¹Assistant Professor, DBT College of Engineering

²Under Graduate Student, DBT College of Engineering

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Abstract - Convolutional Neural Networks are now being used to solve an increasing number of object recognition tasks (CNN). Convolutional neural networks have improved most current new computer vision tasks have emerged due to their high remembrance rate and quick attainment. In this paper, we propose a convolutional neural network implementation of a traffic sign recognition algorithm. TSR (traffic-sign recognition) is an important component of driver-assistance systems that can help drivers avoid a wide range of possible hazards and enhance their driving experience. The TSR, on the other hand, is a practical challenge with numerous limitations, including the visual environment, physical losses, and partial occasions, among others. Convolutional neural networks were developed to deal with the constraints. Convolutional neural networks (CNN) are used to extract visual features of traffic signs and classify them into corresponding groups in order to deal with the constraints.

Key Words:  Machine Learning, Customer Segmentation, Artificial Intelligence.