Advanced traffic light control system using barrier gate and GSM

Kumaar, M. Ashwin; Kumar, G. Akshay; Shyni, S. M.

doi:10.1109/iccpeic.2016.7557213

Cited by 8 publications

(5 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Scientific proponent of this research refer to research about CNN for face recognition focusing on application development using CNN algorithm and evaluate from the results of systems that have been developed using different light intensity [10]. Then a literature review that supports the control system using a barrier gate which is implemented in the IoT model is research focuses on developing a light traffic system using barrier gates and GSM, which models the traffic light system by combining cameras, motor, buzzers, traffic lights, and microcontrollers to produce a vehicle control simulation that can help the police at a relatively low cost [11].…”

Section: Related Researchmentioning

confidence: 99%

Face Recognition Using the Convolutional Neural Network for Barrier Gate System

Prasetyo

Wibowo

Ridwan

et al. 2021

Int. J. Interact. Mob. Technol.

View full text Add to dashboard Cite

The implementation of face recognition technique using CCTV is able to prevent unauthorized person enter the gate. Face recognition can be used for authentication, which can be implemented for preventing of criminal incidents. This re-search proposed a face recognition system using convolutional neural network to open and close the real-time barrier gate. The process consists of a convolutional layer, pooling layer, max pooling, flattening, and fully connected layer for detecting a face. The information was sent to the microcontroller using Internet of Thing (IoT) for controlling the barrier gate. The face recognition results are used to open or close the gate in the real time. The experimental results obtained average error rate of 0.320 and the accuracy of success rate is about 93.3%. The average response time required by microcontroller is about 0.562ms. The simulation result show that the face recognition technique using CNN is highly recommended to be implemented in barrier gate system.

show abstract

Section: Related Researchmentioning

confidence: 99%

Face Recognition Using the Convolutional Neural Network for Barrier Gate System

Prasetyo

Wibowo

Ridwan

et al. 2021

Int. J. Interact. Mob. Technol.

View full text Add to dashboard Cite

show abstract

“…The code will be compiled by using the HITECHC compiler. The working principle of IRsensors is similar to switches [10], also isimpossible to interface sensor in a simulation circuit.LED lights have been interfaced across each road torepresent the signal system. LCD display is used todisplay the waiting time for each road.…”

Section: Circuit Diagram Of the Proposed Systemmentioning

confidence: 99%

Density Based Traffic Signal System Using Arduino

B¹,

Jenifer²,

C³

et al. 2020

IJRTER

View full text Add to dashboard Cite

Traffic congestion is a severe problem in many major cities across the world and it has become a nightmare for the commuters in these cities. Traffic can be controlled in several main junctions by incorporating either automatic traffic light control or traffic police. But conventional traffic light system is based on fixed time concept allotted to each side of the junction which cannot be varied as per varying traffic density. At some times, priority of traffic light needs to be changed based on more number of vehicles waiting in same road, VIPs vehicles and Ambulance vehicles etc. We propose to design and develop a density based traffic signal system. The signal changes automatically on sensing the traffic density at the junction. The prototype model was developed using IR sensors and Arduino. We use Arduino to write programming according to our requirements due to its simplicity and economy and IR sensors is used to measure the traffic density in a particular road. IR sensors may have limitations that it will work in normal light also. As a result, traffic light works in improper way. In future, it may be improved by using some suitable sensors. IR sensors are arranged on each road in accurate manner to detect traffic density properly; these sensors always sense the traffic on that particular road. All these sensors are interfaced to the arduino Based on these sensors, controller detects the traffic and controls the traffic system. The controls of traffic light depend on number of vehicles available in the road.

show abstract

“…traffic control frameworks that ambulances are getting made up for lost time by a red traffic sign and burning through significant time. [1][2][3][4] Blockage is frequently converted into lost time, missed opportunities, lost specialist efficiency, conveyance delay and, as a rule expanded cost.…”

Section: Introductionmentioning

confidence: 99%

Life Saving Ambulance Monitoring Using Image Processing

Ramya¹,

Priyadarshini²,

K³

et al. 2020

IRJASH

View full text Add to dashboard Cite

Traffic clog and tidal stream the executives were perceived as serious issues in current urban regions, which have caused a lot of awkward for the rescue vehicle. The thought behind this plan is to execute an ITS which would control precisely the traffic lights dependent on the quantity of vehicle present in the way and furthermore dependent on the emergency vehicle signal. This plan is completely computerized, in this manner it finds the mishap spot, controls the traffic lights, assisting with arriving at the emergency clinic in time.

show abstract

Advanced traffic light control system using barrier gate and GSM

Cited by 8 publications

References 0 publications

Face Recognition Using the Convolutional Neural Network for Barrier Gate System

Face Recognition Using the Convolutional Neural Network for Barrier Gate System

Density Based Traffic Signal System Using Arduino

Life Saving Ambulance Monitoring Using Image Processing

Contact Info

Product

Resources

About