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Vehicle Identification and Surveillance Using Efficient Machine Learning Techniques | IEEE Conference Publication | IEEE Xplore

Vehicle Identification and Surveillance Using Efficient Machine Learning Techniques


Abstract:

Identification of vehicles is an important area of research. It has several applications namely toll gate fee management systems, automatic parking applications, theft pr...Show More

Abstract:

Identification of vehicles is an important area of research. It has several applications namely toll gate fee management systems, automatic parking applications, theft prevention, traffic monitoring and ticket(fine) imposing etc. Presently, there are many vehicles manufacturers marketing various types of vehicles, and this has an increasing growth rate. It has become a difficult task to analyze various attributes of vehicles and to identify each one of them. Vehicle identification is the main objective of this project. Using license plate, different vehicles can be identified but it is troublesome when the license plate recognition fails in scenarios where the license plate is forged, missing, or covered. The attributes such as the model and make of the vehicle cannot be easily changed and this helps in recognition of vehicles easily. In this project, vehicle recognition mainly focuses on identifying the name of the vehicle manufacturing company. Further recognition is done by the extraction of the license plate number by employing Haar Cascade classifier.
Date of Conference: 16-17 June 2023
Date Added to IEEE Xplore: 07 August 2023
ISBN Information:
Conference Location: Coimbatore, India

I. Introduction

Vehicle recognition on a general view, has impact on several applications such as in cases where vehicles that were stolen can be obtained back by identifying the license plate details, vehicle model details and matching them with the data at the existing databases that contains the registered data of the vehicles of a particular region. This system can also be helpful in scenarios where vehicle parking lots can be automatically allocated by identifying the vehicle model, its license plate number and the size of the vehicle. This results in the reduction of the parking spaces, without the call for manual intervention. This can also help in classifying the road lanes by categorizing lanes depending on the vehicle's model, height, speed. The vehicle recognition as seen above as various use cases and can combat the challenges in corresponding fields.

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References

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