Implementation of image processing in real time traffic light control

Choudekar, Pallavi; Banerjee, Sayanti; Muju, M. K.

doi:10.1109/icectech.2011.5941662

Cited by 32 publications

(16 citation statements)

References 4 publications

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“…Setting image of an empty road as reference image, the captured images are sequentially matched using image matching. The Prewitt edge detection operator has been carried out and according to percentage of matching traffic light durations can be controlled [4].…”

Section: Introductionmentioning

confidence: 99%

Area Based Traffic Control System

Win¹,

Win²,

Theingi³

2018

IJSRP

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An efficient traffic control system is an important issue to reduce the traffic congestion. In this research, area based traffic control system is implemented for four-way intersection by using image processing techniques. This system is a vehicle-actuated signal type control system such that traffic cycle time can be varied and related to the actual demands by traffic. The CCTV traffic video file at a station in Mandalay is utilized as the input for the image processing. The amount of vehicles bounded on the road is estimated and defined as the traffic area with the numbers of pixels. Depending on sum of the amount of area on both opposite directions, three types of traffic cycle times are determined such as 60 sec, 85 sec and 120 sec respectively. The vehicles areas on the analyzed road are calculated by using contour, moment and area-based calculation image processing algorithms. The experimental results are implemented with the help of OpenCV and simulation of traffic signal timing sequence is tested with Arduino.

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Section: Introductionmentioning

confidence: 99%

Area Based Traffic Control System

Win¹,

Win²,

Theingi³

2018

IJSRP

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“…In another work, vehicles were detected by the Prewitt edge detector and image matching and the traffic light duration was determined based on the percentage of matching [19].…”

Section: Introductionmentioning

confidence: 99%

Traffic light control based on fuzzy Q-leaming

Moghaddam

Hosseini

Safabakhsh

2015

2015 the International Symposium on Artificial Intelligence and Signal Processing (AISP)

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Traffic is an issue that many big cities are confronted with because of ever-increasing population growth. In this paper we propose a two phase traffic light control system based on fuzzy Q-learning for an isolated 4-way intersection. The states and actions of the Q-learning variables is set by a fuzzy algorithm which can be learned through environmental interactions and taking advantage of fuzzy logic. The proposed algorithm was simulated for a period of one hour for each of 14 different traffic conditions. Comparison with other methods was carried out on the 14 traffic conditions. The results showed that the proposed algorithms decrease the total waiting time and the mean of queue length.Index Terms-Fuzzy logic, fuzzy Q-learning, traffic light control.

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“…Despite being an alternative that models the nonlinear traffic dynamics with precision, the practical deployment of such a strategy is complicated because of the required process of acquiring information. [4][5][6] use image processing techniques to monitor the traffic and enable a more efficient control due to the higher quantity of available information. Choudekar [4] control the traffic in a reactive way, using the images to determine the vehicle density in a street and adjust the green time of the semaphore based on it.…”

Section: Introductionmentioning

confidence: 99%

“…[4][5][6] use image processing techniques to monitor the traffic and enable a more efficient control due to the higher quantity of available information. Choudekar [4] control the traffic in a reactive way, using the images to determine the vehicle density in a street and adjust the green time of the semaphore based on it. Kumar et al [5] develops vehicle behavior recognition in order to predict its trajectory.…”

Section: Introductionmentioning

confidence: 99%

Multilayer distributed model predictive control of urban traffic

Castro

Martini

Hirakawa

2013

WIT Transactions on Ecology and the Environment

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Urban traffic is a relevant topic for the population of cities that suffer from daily traffic jams. People not only spend time on their journeys but also lose energy. With the purpose of improving the traffic flow, it is possible to control the temporization of traffic lights. However, because of the intrinsic characteristics of the system-multivariable, stochastic and dynamic-controlling the traffic of a city through the temporization of its actuators is insufficient to achieve an optimal level. This way, the objective of this work is to elaborate a multilayer distributed model predictive control (ML-DMPC) of the traffic. The proposed model is composed of two layers of control: local and global. Each agent of control is responsible for the local control logic of a set of interdependent semaphores-in other words, semaphores that belong to the same intersection between streets. In order to achieve the local control objective, each control agent is modelled as a neural network and the following components are weighted: the waiting time of the vehicles; the waiting time of the pedestrians; and synchronization of the semaphores. The input variables of the system-flow of vehicles and pedestrians in all directions-are treated by fuzzy logic for the purpose of improving the quality of the information. The principles of consensus between control agents and behavioural coordination are used to conciliate local and global control objectives. In the global control layer, reinforcement learning is applied to improve the modelling process of complex dynamic systems, as the city traffic, as well as to provide adaptability to the model through learning. By means of the proposed model, it is possible to achieve the dynamic control of the traffic lights system of a city.

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Implementation of image processing in real time traffic light control

Cited by 32 publications

References 4 publications

Area Based Traffic Control System

Area Based Traffic Control System

Traffic light control based on fuzzy Q-leaming

Multilayer distributed model predictive control of urban traffic

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