Review Paper
Smart traffic manager: Computer vision and deep learning-based approach
In the last couple of decades, the number of vehicles has been on the road increased drastically. Hence it has become very difficult to keep track of every vehicle for traffic management and law enforcement. With the increasing number of vehicles on roads, it is getting difficult to manually enforce laws and traffic rules for smooth traffic flow. Traffic Management systems are installed on traffic signals to check for vehicles breaking the traffic rules. To automate all these processes a system is required to easily identify a vehicle. The main aim to design this system is to reduce the mishaps which occur due to reckless driving and violations of the traffic rules. The important question here is how to identify a particular vehicle, The obvious answer to this question is by using the vehicle’s registered license plate as every vehicle has a unique number through which it is easily differentiated from all the other vehicles. Vehicles in each country have a unique license number, which is written on their registered number plate. This number distinguishes one vehicle from the other, which is useful especially when both are of the same make and model. So, the basic idea will be identifying whether the two-wheeler rider is wearing a helmet or not, over speeding vehicles, zebra crossing violators, etc. Most of the tasks in this will require machine learning/deep learning models for image processing tasks. In the end, this system would be very effective to automate the hectic task of the traffic police and can be very efficient in terms to reduce the workload and manage the different tasks autonomously.
Published by: Sahil Shaikh, Swapnil Patil, Niranjan Patil, Purushottam Kulkarni, J. K. Kamble
Author: Sahil Shaikh
Paper ID: V7I1-1236
Paper Status: published
Published: February 15, 2021
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