Smart Penalty System (SPS) in Traffic Signals

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Abstract

Abbreviations

Market Trends and Challenges

Solution

Common Issues

Conclusion

Reference Reference

Author Info

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Table of Contents


One of the major impacts of traffic violations on the road are traffic jams, especially in metropolitan cities such as Chennai, Bangalore, Mumbai and New Delhi. It is nearly impossible to control the explosion of traffic in such areas, and consequently, smaller traffic violations at the signals often go unnoticed. Normally, people take advantage of this and try to cross the road at the signals before the light turns green. Only by levying a reason-able penalty on those who violate traffic rules, we would be able to control the city traffic to a good extent. However, amidst so many fast-moving vehicles, it becomes extremely difficult to stop the violating vehicles in the middle of traffic to enforce the law as that only adds to the frustration among commuters.the middle of traffic to enforce the law as that only adds to the frustration among commuters. Through this whitepaper, we are thus proposing a Smart Penalty System (SPS), which has the potential to solve prevalent violation issues through the automation of the penalty system. The SPS uses pattern recognition to identify the vehicle license number plate and automatically sends the penalty to the owner of the vehicle.

The current penalty system, being manual, requires a huge number of human resources, such as the traffic police. Even though the current system is working well, incidents of violation are not reducing since it is not possible to monitor all of the traffic efficiently. Even if we were able to monitor and cover an entire area of moving traffic by installing cameras, we cannot stop everybody from violating rules nor penalize them amidst such heavy traffic. This would lead to other unethical issues, such as taking bribes.

There are many reasons why we need an automated penalty system.

The increasing amount of traffic on roads due to the growing number of vehicles.

Traffic peak hours being extended to the entire day.

It is not possible to monitor the entire stretch of roadways. By using human resources we can control or monitor traffic only at the signals or at some of the most highly populated areas.

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SPS (Page no: 4)

ALPR (Page No: 6)

Smart Penalty System

Automatic License Plate Recognition

Full FormAcronyms (Page No.)

Abstract

Abbreviations

Market Trends and Challenges

Smart Penalty System (SPS) in Traffic Signals | 3


The current system contains many gaps allowing violators to escape from paying penalties. For example, if a person is warned and asked to pay the penalty for violating traffic rules at one signal, and he/she repeats the same violation at another signal, when apprehended by the traffic police, he/she could show the penalty slip to the cops and escape any additional penalty. This can be avoided through the automated system.

TheThe SPS can be implemented for the entire road network. There are many ways to identify traffic violations, such as sudden lane switching, riding without helmets, parking violations, over-speeding, and more. This can be a final solution for traffic control. Additionally, human resources can be diverted for other more important purposes.

We recommend automating the existing traffic penalty system. The investments would only require placing cameras at signals that are controlled by sensors (IR sensors). An organized database of all registered vehicles along with owner details would help in more accurate automation.

Figure 1: Penalty System block diagram

Solutions

Sensor Control Unit

Camera Controlunit

Traffic Signal logiccontroller

Traffic Signal

IRTXR

IRRXR

Central Controlunit withpatternRecognition

Penaltycomputationand SMSsystem

SPS – Smart Penalty System



Figure 1 describes the penalty system. If any vehicle moves when the red signal is active, the IR sensor will detect and trigger the camera, which takes a snapshot of the vehicle’s number plate. The pattern recognition system identifies the vehicle number and finds the registration details. It can track previous violations too. Based on this data, a reasonable penalty can be calculated and the information would be sent to the vehicle owner’s mobile number.

We can implement this in 3 ways, and all of them provide different advantages.

Common CCTV cameras

ANPR cameras (HCL has an ANPR product)

Motion capture ANPR cameras

Each requires its own digital processing method and has different costs for the solution. Using Common CCTV cameras is the minimum cost solution, where we might need to implement the entire plate recognition method as follows:

This can be used in cities with a normal traffic flow.

Using ANPR cameras might increase the cost, but it is faster and requires less implementation costs. Motion capture ANPR cameras can be used to detect vehicles that are over-speeding.

Plate Localization NormalizationCharacterSegmentation

CharacterRecognition

Syntactial/GeoRule

Search EnginePenaltyTriggeringSystem

Plate Orientationand Sizing

Figure 2: Plate Recognition method



However, there are some practical limitations to implementing these methods effectively.

SPS goes a long way in automating the much needed vehicle traffic violation system. It has great potential to increase compliance to traffic signals at intersections and to also do away with manpower, thereby eliminating errors.

AA smart red light violation detection system has been implemented in Trivandrum, Calicut, Kochi and Indore, where cameras have been installed at signal intersections and automated slip-generation occurs. However, people are able to receive these slips with captured picture evidence after a considerable time lag. The SPS can eliminate time lags with an updated database and communications system that can send immediate messages to the violating vehicle owner’s registered mobile number.

Nowadays,Nowadays, we have ALPR cameras (Automatic License Plate Recognition) and they can serve better than normal CCTV cameras. Once the traffic is controlled at the signals, we can expand this to the entire city so that we can monitor the entire road network.

But even if we are able to monitor the entire road network and control traffic by enforcing penalties through automated systems like the SPS, the real control still lies with the person driving the vehicle!

They require a reasonable investment for new camera sensor systems, making the system expensive

The system requires regular field checking for sensor performance

The recognition system must be highly reliable or it may lead to wrong results

Even though we do not need manpower in the traffic area, we do need them to monitor the data captured through the SPS and effectively collect the penalties

ThisThis system can be improved only if we implement it for all vehicles including government vehicles and municipal buses, which present some complicated scenarios as to who will pay the penalties - the trans-port department or the driver of the vehicle

This does not provide any solution for pedestrians who violate traffic rules

Common Issues

Conclusion



This whitepaper is published by HCL Engineering and R&D Services.

The views and opinions in this article are for informational purposes only and should not be considered as a substitute for professional business advice. The use herein of any trademarks is not an assertion of ownership of such trademarks by HCL nor intended to imply any association between HCL and lawful owners of such trademarks.

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Hello, I’m from HCL’s Engineering and R&D Services. We enable technology led organizations to go to market with innovative products and solutions. We patner with our customers in building world class products and creating associated solution delivery ecosystems to help bring market leadership. We develop engineering products, solutions and platforms across Aerospace and Defense, Automotive, Consumer Electronics, Software, Online, Industrial Manufacturing, Medical Devices, Networking and Telecom, Office Automation, Semiconductor and Servers & Storage for our customers.

Reference

Author InfoGanesan C

HCL Engineering and R&D Services

1. ALPR - http://en.wikipedia.org/wiki/Automatic_number_plate_recognition

2. www.motorolasolutions.com/US-EN/Business+Product+and+

Sevices/Software+and+Applications/Public+Sector+Applications/Automatic%20License%20Plate%20Recognition_US-EN

3. ‘Real time number plate localization algorithms’ – Balazs Enyedi, Lajos Konyha and Kalman Fazekas.

4. ‘A Design flow for Robust License Plate Localization and Recognition in Complex scenes’ – Dhawal Wazalwar, Erdal Oruklu and

Jafar Saniie.

5. ‘Segmentation Methods for Character Recognition: From Segmentation to Document Structure Analysis’ – Hiromichi Fujisawa, 5. ‘Segmentation Methods for Character Recognition: From Segmentation to Document Structure Analysis’ – Hiromichi Fujisawa,

Yasuaki Nakano and Kiyomichi Kurino.

6. ‘Automatic Number Plate Recognition System’ – Hakob Sarukhanyan, Souren Alaverdyan, and Grigor Petrosyan.

http://www.motorolasolutions.com/US-EN/Business+Product+and+Services/Software+and+Applications/Public+Sector+Applications/Automatic%20License%20Plate%20Recognition_US-EN