Intelligent Traffic Light System Using Computer Vision with Android Monitoring and Control

Nodado, Jess Tyron G.; Morales, Hans Christian P.; Abugan, Ma Angelica P.; Olisea, Jerick L.; Aralar, Angelo C.; Loresco, Pocholo James M.

doi:10.1109/tencon.2018.8650084

Cited by 18 publications

(9 citation statements)

References 2 publications

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“…An image with an empty road and compares it with a newly captured image to find the density of cars. Nodado et al [16] developed a system that employs CCTV cameras stationed at every lane of the intersection for the capturing of traffic pictures. The pictures can then be sent to the Raspberry Pi 3 micro-controller for traffic density calculation.…”

Section: Related Workmentioning

confidence: 99%

Integrated tripartite modules for intelligent traffic light system

Kareem

Hoomod

2022

IJECE

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<span>The traffic in urban areas is primarily controlled by traffic lights, contributing to the excessive, if not properly installed, long waiting times for vehicles. The condition is compounded by the increasing number of road accidents involving pedestrians in cities across the world. Thus, this work presents an integrated tripartite module for an intelligent traffic light system. This system has enough ingredients for success that can solve the above challenges. The proposed system has three modules: the intelligent visual monitoring module, intelligent traffic light control module, and the intelligent recommendation module for emergency vehicles. The monitor module is a visual module capable of identifying the conditions of traffic in the streets. The intelligent traffic light control module configures many intersections in a city to improve the flow of vehicles. Finally, the intelligent recommendation module for emergency vehicles offers an optimal path for emergency vehicles. The evaluation of the proposed system has been carried out in Al-Sader city/Bagdad/Iraq. The intelligent recommendation module for the emergency vehicles module shows that the optimization rate average for the optimal path was in range 67.13% to 92%, where the intelligent traffic light control module shows that the optimization ratio was in range 86% to 91.8%.</span>

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Section: Related Workmentioning

confidence: 99%

Integrated tripartite modules for intelligent traffic light system

Kareem

Hoomod

2022

IJECE

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“…with Linux as an embedded operating system are quite common and offer appropriate computing performance. The embedded microcontroller technology could be used for various lighting control purposes in various fields, in building and civil engineering [32], in vision research [33,34], in agriculture [35], in smart home [36], etc. In recent years, interesting applications in lighting control and energy-saving are implemented based on Arduino micro-controller, such as the work of Yin et al [37], based on Arduino Mega 2560.…”

Section: Related Workmentioning

confidence: 99%

DALI LED Driver Control System for Lighting Operations Based on Raspberry Pi and Kernel Modules

Adam

2019

Electronics

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Light emitting diodes (LEDs) as an efficient low-consumption lighting technology are being used increasingly in many applications. The move to LED lighting is also changing the way the lighting control systems are designed. Currently, most electronic ballasts and other digital lighting devices implement the Digital Addressable Lighting Interface (DALI) standard. This paper presents a low-cost, low-power effective DALI LED driver controller, based on open-source Raspberry Pi3 microcontroller prototyping platform. The control software is developed as a Linux kernel module under UBUNTU 18.04.2 LTS patched with PREEMPT_RT (Preemptive Real-time) for real-time processing. This dynamically loaded kernel module performs all the processing, communication and control operations of the Raspberry Pi3-based DALI controller with the DALI LED driver and LED luminaire. Software applications written in C and Python were developed for performance testing purposes. The experimental results showed that the proposed system could efficiently and effectively manage DALI LED drivers and perform lighting operations (e.g. dimming). The system can be used for a variety of purposes from personal lighting control needs and experimental research in control of electronic ballasts and other control gears, devices and sensors, to advanced requirements in professional buildings, including energy management, lighting maintenance and usage.

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“…In this short communication, we propose a feasible solution for heavy goods vehicle detection. Computer Vision algorithms have been implemented for various tasks in traffic monitoring for many years, e.g., traffic sign recognition [1][2][3][4][5][6][7]; intelligent traffic light system [8]; vehicle speed monitoring [9]; traffic violation monitoring [10]; vehicle tracking [11][12][13]; vehicle classification [14][15][16][17][18][19][20][21][22][23][24][25][26]; vehicle counting system on streets and highways [27][28][29][30][31]; parking spot detection from the point of view of the car for parking assistants [32,33]; and parking spot monitoring [34][35][36][37][38][39][40][41][42][43][44][45][46][47]…”

Section: Introductionmentioning

confidence: 99%

Short Communication: Detecting Heavy Goods Vehicles in Rest Areas in Winter Conditions Using YOLOv5

et al. 2021

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The proper planning of rest periods in response to the availability of parking spaces at rest areas is an important issue for haulage companies as well as traffic and road administrations. We present a case study of how You Only Look Once (YOLO)v5 can be implemented to detect heavy goods vehicles at rest areas during winter to allow for the real-time prediction of parking spot occupancy. Snowy conditions and the polar night in winter typically pose some challenges for image recognition, hence we use thermal network cameras. As these images typically have a high number of overlaps and cut-offs of vehicles, we applied transfer learning to YOLOv5 to investigate whether the front cabin and the rear are suitable features for heavy goods vehicle recognition. Our results show that the trained algorithm can detect the front cabin of heavy goods vehicles with high confidence, while detecting the rear seems more difficult, especially when located far away from the camera. In conclusion, we firstly show an improvement in detecting heavy goods vehicles using their front and rear instead of the whole vehicle, when winter conditions result in challenging images with a high number of overlaps and cut-offs, and secondly, we show thermal network imaging to be promising in vehicle detection.

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Intelligent Traffic Light System Using Computer Vision with Android Monitoring and Control

Cited by 18 publications

References 2 publications

Integrated tripartite modules for intelligent traffic light system

Integrated tripartite modules for intelligent traffic light system

DALI LED Driver Control System for Lighting Operations Based on Raspberry Pi and Kernel Modules

Short Communication: Detecting Heavy Goods Vehicles in Rest Areas in Winter Conditions Using YOLOv5

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