Hybrid Approach for Detection and Recognition of Vehicles
- 1 , India
Copyright: © 2020 K. S. Selvanayaki and Rm. SomaSundaram. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
On road vehicles have increased in numbers and monitoring them is a challenging task. In common areas of public crowd people and vehicles are common objects for monitoring. The proposed system aims at detecting number plate information indicating the possibility for security relevant issues. Existing system performs recognition mainly by using license plate alone. Addition of the features (logo, colour, shape) will increase the security of the system. Identification of the number plate region has been done by Blob detection method at the predefined aspect ratio. After detection, extraction of the number plate information using Eigen value regularization method. Further, two methodology included in this study are, identifying the tampered region in a car image either by extracting HoG feature in the spatial domain or block differences in DCT coefficients and their corresponding histogram in the transform domain respectively. Experimental results for the given car dataset describes the identification of the number plate region and tampered region quantitatively. The work presents detailed results of how the proposed approach gives better results using HoG approach. The approach gives good results in videos of cars recorded in frontal view in good lighting conditions. The paper in overall suggest a hybrid approach for detecting number plate information in cars taken in good lighting conditions.
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- License Plate Detection
- Character Extraction and Detection
- Eigen Value Regularization
- Histogram of Gradient (HoG)
- Discrete Cosine Transformation (DCT)