The Proficient ML method for Vehicle Detection and Recognition in Video Sequence

Deep Learning has become a highly strong technique and capacity to manage enormous volumes of data over the last few decades. Convolutional Neural Networks are a type of deep neural network that is quite popular. Traffic difficulties are becoming more prevalent as the number of people and cars on th...

Full description

Saved in:
Bibliographic Details
Published in2021 International Conference on System, Computation, Automation and Networking (ICSCAN) pp. 1 - 5
Main Authors Saranya, D., Kanagavalli, N., Priyaradhikadevi, T.
Format Conference Proceeding
LanguageEnglish
Published IEEE 30.07.2021
Subjects
Character recognition
Character segmentation
Computational modeling
Connected Component Analysis
Deep learning
Licenses
Machine Learning
Neural networks
Training
Vehicle detection
Video sequences
Online AccessGet full text

Cover

More Information
Summary:Deep Learning has become a highly strong technique and capacity to manage enormous volumes of data over the last few decades. Convolutional Neural Networks are a type of deep neural network that is quite popular. Traffic difficulties are becoming more prevalent as the number of people and cars on the road grows. Machine learning is currently being used to tackle some of these issues, including precise bridge and highway toll lanes, parking lot management, and traffic prevention. This research will look at how deep learning may be used to monitor cars and recognize license plates. This research implemented the CNN, Connected Component Analysis, and segmentation technique for vehicle detection and vehicle number plate detection. The proposed model gives the fundamentals of current neural networks and how they interact with computer vision applications. It can increase the accuracy of the proposed model with a pre-trained model by employing the key components of neural networks. Finally, applying the CNN model obtained more accuracy in the character recognition phase.
DOI:10.1109/ICSCAN53069.2021.9526504