International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 11 Issue: 06 | Jun 2024
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p-ISSN: 2395-0072
DENSITY BASED TRAFFIC LIGHT CONTROL USING TURTLE GRAPHICS Sakshi N. Mahajan1, Sakshi L. Karale2, Shilpa B. Patil3,Prof. D. O. shirsath4 123BTECH Student, 4Assistant Professor
Department of Electronics and Telecommunication Engineering, Padmbhooshan Vasantraodada Patil Institute of Technology, Budhgaon(Sangli) ---------------------------------------------------------------------***--------------------------------------------------------------------Abstract: The significant growth in the number of vehicles and the long intervals between traffic lights make traffic control more challenging nowadays. To address this issue, we can implement an image-processing-based traffic-light control system. In place of electronic sensors, the proposed system will detect vehicles through images. The cameras positioned next to the traffic signal will take pictures of the lanes, and the Image Processing technique in Python may be used to calculate the number of vehicles in each lane. And the lane with the highest count will take precedence over the other lanes. The traffic light is regulated by the amount of traffic on the road, thus using this technology is beneficial for analysis and performance. With the modern world continuously becoming very fast-paced each and every person is always trying to make the most of his time. It is very much required that any person doesn't waste a lot of his crucial time on a petty activity like traveling. Along with this driving in streets with a lot of traffic has been scientifically proven to be the cause of very high mental strain and pressure. So it is a basic requirement in modernday cities to have a dynamic model of the traffic signal to control the transportation in the area
or equally empty. Thus the average waiting time of all the people at the traffic light is reduced.
2 LITERATURE REVIEW Dynamic Traffic System Based On Real Time Detection of Traffic Congestion[1], The paper proposes a dynamic traffic system that takes in present traffic footage and calculates the percentage congestion and based on this, allocates the timer to each signal.Uses image processing for Background subtraction and Edge Detection. Smart Traffic Light Switching/Traffic Density Calculation using Video Processing[2], This paper presents a method to use live video feed from the cameras at traffic junctions for real time traffic density calculation using video and image processing. Used for 4 way junction detection which shows 35% improvement in congestion and allows Traffic light synchronization enabling free flow of traffic.
Keywords: Traffic signal control, image processing, object detection, YOLOv4, traffic light
Density and Time based Traffic Control System using Video Processing [3], This paper discusses the idea of a traffic signal system by detecting traffic density and adjusting the signal accordingly. Uses haar cascade algorithm providing high accuracy.
1.INTRODUCTION
3 PROPOSED METHODOLOGIES
Coming to the present case of traffic lights across the cities, it can be noted that it is static and is always the same for any lane, even though the traffic in those lanes may not be the same. This causes some lanes to become empty while some lanes are too congested and this is a waste of time for people and also a waste of resources as a lot of fuel is wasted while not moving.
Existing System:
Along with this in congested lanes the traffic light is for a very less duration, so many people tend to cut the traffic light and this may be the cause of various road accidents. Coming to the most important point of why we require dynamic traffic lights is that there are many emergencies occurring and it is required for emergency vehicles like police, ambulance, and fire trucks to reach these situations in the proper time as this is the case of life or death. For all these reasons we can say that dynamic traffic control is the best solution. In a dynamic traffic light the waiting time of a signal changes with respect to the number of cars in the lane and thus nearly all the lanes become equally congested
Cascade Classifier Training.
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OpenCV accompanies a coach just as identifier. In the event that you need to prepare your own classifier for any article like vehicle, planes and so on you can utilize OpenCV to make one. Its full subtleties are given here:
Proposed Solution: After preprocessing like resizing and cropped images, Haar cascade classifier is used to detect whether there is a single face detected or not. Figure 3 demonstrates the flow chart for the proposed system. Edge, line, and center surround are the features of Haar which are acting as inputs. By these cascade features the test of the image is done. The features of Haar are divided into various different stages. Stage by stage the window will be tested. Usually, initial stages will
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