Image Processing Technique for Traffic Density Estimation

Traffic congestion has become a significant problem in recent years. Efficient traffic control system by detecting and counting the vehicle numbers at various times and locations are required. Traffic estimate from the static images is the key issue for automating traffic light controls. In this work, we address the problem of estimating the traffic congestion and vehicles density on the roads. Based on study of available methods in estimating the traffic density, we tried to modify some of the algorithms to improve the congestion estimate. In visual surveillance applications, detection by background subtraction is a common approach for differentiating moving objects from the static parts of the video frames. One of the ways to overcome traffic problems in large cities is through the development of an intelligent traffic control system which is based on the measurement of traffic density on the road. The system is implemented and simulated in Matlab, and it performance is tested on real video. It is observed from the experiment that based effective density in the road ,the traffic can be controlled effectively.

International Journal for Research in Applied Science and Engineering Technology

In today's life we have to face different types of problems one of which is increasing number of vehicles. Due to which it increases traffic and congestion. The solution is to conquer the traffic congestion is the Intelligent Traffic control system. The real time image processing is used to measuring the traffic density on that road. The various research papers are used to sum up an overview on different strategies for building up a Smart Traffic Control System for Traffic Density Count. For Traffic Density Count, it likewise demonstrates an analysis on various strategies under the image processing. Different authors utilized different techniques like identifying number of vehicles from the video utilizing the camera mounted at path and some utilizes live video for Traffic Density count using the image processing and video or utilize remote sensors to detect presence of traffic. This paper shows the comparison and survey of all these methods.

International Journal of Computer Applications, 2013

Due to the increase in the number of vehicles day by day, traffic congestions and traffic jams are very common. One method to overcome the traffic problem is to develop an intelligent traffic control system which is based on the measurement of traffic density on the road using real time video and image processing techniques. The theme is to control the traffic by determining the traffic density on each side of the road and control the traffic signal intelligently by using the density information. In this paper we proposed the algorithm to determine the number of vehicles on the road. The density counting algorithm works by comparing the real time frame of live video by the reference image and by searching vehicles only in the region of interest (i.e. road area). The computed vehicle density can be compared with other direction of the traffic in order to control the traffic signal smartly.

— From city roads to highways, a lot of traffic problems occur everywhere in today's world. An Excessive number of vehicles on roads and improper methods of controlling the traffic creates traffic congestion which leads to wastage of time and increases the pollution. This frequent traffic jams problem leads to a rise of the need for an efficient and proper traffic management system. Existing methods work well in free-flow traffic but in the case of heavy congestion, these methods face challenges. Current traffic control techniques involving magnetic loop detectors buried inside the road, infra-red and radar sensors on the side provide limited traffic information. The use of image processing helps in the proper management of traffic even in shadows and various lighting conditions and it is also cost effective as the devices it requires like sensors, cameras are cheaper than for other solutions. Comparison and survey of all these methods is shown in this paper which concluded that use of canny edge detection method makes an analysis of traffic comparatively efficient.

The activity on the streets is builds step by step. The need of creating framework that can oversee and control the movement on street. The movement of numerous vehicle on streets is additionally critical for taking different choices identified with activity. The framework brings an activity picture from a CCTV camera to process in the framework as an information. From that point onward, the framework finds for movement clog and gets the outcomes in three rush hour gridlock conditions as Flow, Heavy, and Jammed. At long last, a client can utilize the framework for a transportation arranging or a convergence movement control. For execution, the framework utilizes a picture preparing system to dissect for a movement condition. It recognizes what number of items or autos out and about. And after that, the framework associates a movement condition result with a database for a transportation arranging.

Advances in Information Technology, 2014

Traffic congestion has been increasing because of increased population growth mainly in major cities due to urbanization. Traffic congestion causes increased air pollution, travel time and mostly traffic accidents; therefore we need an efficient traffic management system. Today most of the cities of the world have intelligent transport system which is equipped with electronics devices to communicate about the traffic condition with the moving vehicle and also monitor the traffic rules and regulation. Current traffic control techniques involving magnetic loop detectors buried in the road, infra-red and radar sensors on the side provide limited traffic information and require separate systems for traffic counting and for traffic surveillance. The disadvantages of the existing system are that it requires traffic personnel to monitor the traffic and magnetic loop detectors cause's high failure rate when installed on the road surfaces. In contrast, video-based systems offer many advantages compared to traditional techniques. They provide more traffic information, combine both surveillance and traffic control technologies, are easily installed, and are scalable with progress in image processing techniques. Implementation of the project will eliminate the need of traffic personnel at various junctions for regulating traffic. Thus the use of this technology is valuable for the analysis and performance improvement of road traffic. Traffic monitoring based on density of vehicles improve the traffic control system by calculating the density of vehicles on the road. This Project proposes to control the traffic using image processing algorithms and embedded systems to control the traffic signals. The videos taken by a camera is analyzed using Background Subtraction method to detect, track and count the number of vehicles moving in each lane to obtain the most efficient traffic management. The vehicle classification and speed detection is also done, such that vehicles are classified into heavy vehicle (trucks, bus) and low vehicle (bikes, cars) and the over speed vehicles are detected and indicated by red boundary.

2019

Vehicle density information for traffic regulation including the timing of traffic lights is still very minimal. This study aims to calculate the number of vehicles at an intersection then classify the density level of each road segment. The detection process begins with taking video from Teling intersection of Manado City, Indonesia. Video processed using the Gaussian Mixture Model (GMM) algorithm and Morphological Operation (MO) to detect vehicles object in the form of BLOB (Binary Large Object). The results of the feature extraction are calculated to get the number of vehicles from the specified Region of Interest (ROI). The results of counting vehicles are classified according to the density level to be able to compare the level of congestion on each road segment. The results of the proposed system accuracy is 90.9% for the calculation of vehicles on the road. This research is expected to be implemented in Smart Traffic Light.

International Research Journal of Modernization in Engineering Technology and Science, 2020

Vehicle counting is viewed as one of the most significant applications in traffic control and the executives.. Vehicle checking measure gives suitable data about traffic stream, vehicle crash events and traffic top occasions in streets. If we have a good sense of the volume of traffic moving along a given road or network of roads, we can better understand congestion and then manage and/or make plans to reduce/eliminate it. Vehicle count data is very useful to urban city planners and transport authorities. At that point, a little lessening in grouping execution can have genuine monetary misfortunes. Therefore, accuracy and time complexity becomes critical for the traffic system. The algorithm used here will check the quantity of vehicles.. However, it requires large labelled datasets and has restrictions when numerous vehicles are in the scene. Herein, we propose machine learning techniques. The experiments show that our setup is performing as accurately as the existing model with significantly lower labelled datasets We apply Haar Cascade algorithm to determine the number of vehicles in traffic signals. When it is found that the density of vehicles is more in traffic signal it will detect and create an alert.

Frontiers of Computer Science, 2014

Road traffic density has always been a concern in large cities around the world, and many approaches were developed to assist in solving congestions related to slow traffic flow. This work proposes a congestion rate estimation approach that relies on real-time video scenes of road traffic, and was implemented and evaluated on eight different hotspots covering 33 different urban roads. The approach relies on road scene morphology for estimation of vehicles average speed along with measuring the overall video scenes randomness acting as a frame texture analysis indicator. Experimental results shows the feasibility of the proposed approach in reliably estimating traffic density and in providing an early warning to drivers on road conditions, thereby mitigating the negative effect of slow traffic flow on their daily lives.

Millions of vehicles pass via roads and cities every day. Various economic, social and cultural factors affect growth of traffic congestion. The amount of traffic congestion has major impacts on accidents, loss of time, cost of money, delay of emergency, etc. Due to traffic congestions there is a loss in productivity from workers, people lose time, trade opportunities are lost, delivery gets delay, and thereby the costs goes on increasing. To solve these congestion problems it is better to build new facilities and infrastructure but at the same time make it smart. Many traffic light systems operate on a timing mechanism that changes the lights after a given interval. An intelligent traffic light system senses the presence or absence of vehicles and reacts accordingly. The idea behind intelligent traffic systems is that drivers will not spend unnecessary time waiting for the traffic lights to change. An intelligent traffic system detects traffic in many different ways [1].