The Traffic Congestion Investigating System by Image Processing from CCTV Camera

2017 ◽  
Author(s):  
Busarin Eamthanakul ◽  
Mahasak Ketcham ◽  
Narumol Chumuang
Keyword(s):  
Image Processing ◽  
Traffic Congestion ◽  
Cctv Camera

scholarly journals Blockchain: Security Services Provided for the Accident Detection

2020 ◽  
Vol 9 (7) ◽  
pp. 1021-1025
Keyword(s):  
Image Processing ◽  
Traffic Congestion ◽  
Security Services ◽  
Block Chain ◽  
The Many ◽  
Accident Detection ◽  
And Control ◽  
Cctv Camera ◽  
Clear Information ◽  
Valid Form

In the 21st century the traffic congestion is common problem. Due to traffic congestion the accident happened every day. In this paper I proposed to detect the accident on the CCTV camera through video statistics acquisition and image processing and provided the security through the block chain. Now these days it is very important to detect accident video and provided security. Many time people did for own purpose. So always need to provide the security for this kind of CCTV footage in real time. Anyone try and control the video integrity, due to this reason the hash value of the video also change. The hash value is mismatched with the value secure stored inside the blockchain. By using this method, the integrity of video proof cannot be disputable. In this paper, we applied trusted timestamp for verifying the video integrity. Trusted timestamping is an efficient method for verifying unmodified virtual information in a fixed particular point in time. CCTV videos have come to be a valid form of proof in court. Also this method to overcome some of those issues is to get admission to independent facts that such kind of events improves knowledge of what truly occurred in accident. It provides the more and clear information about the accident. After the detecting accident footage decentralized or distributed over the cloud. The security cell gets the data footage or video from cloud. There are the many technique to find detect the accident detection, ex-image processing accident detection, statistics acquisition and image processing ,Adaptive algorithm, Based on the object detection using the machine vision technique

Traffic congestion identification based on image processing

2012 ◽  
Vol 6 (2) ◽  
pp. 153 ◽  
Author(s):  
H. Jianming ◽  
M. Qiang ◽  
W. Qi ◽  
Z. Jiajie ◽  
Z. Yi
Keyword(s):  
Image Processing ◽  
Traffic Congestion

scholarly journals Traffic Light Controller using Image Processing

2021 ◽  
Vol 12 (2) ◽  
pp. 405-411 ◽  
Author(s):  
Lakshmanan M, Et. al.
Keyword(s):  
Image Processing ◽  
Edge Detection ◽  
Real Time ◽  
Traffic Congestion ◽  
Traffic Signals ◽  
Daily Basis ◽  
Traffic Density ◽  
Traffic Light ◽  
Traffic Lights ◽  
Traffic Systems

Traffic congestion at junctions is a serious issue on a daily basis. The prevailing traffic light controllers are unable to manage the different traffic flows. Most of the current systems operate on a timing mechanism that changes the signal after a particular interval of time. This may cause frustration and result in motorist's time waste. Traffic congestion is a major problem in the currently existing systems. Delays, safety, parking, and environmental problems are the main issues of current traffic systems that emit smoke and contribute to increasing Global Warming. Sensor-based systems reduce the waiting time and maximize the total number of vehicles that can cross an intersection. Our proposed system can control the traffic lights based on image processing without the need for traffic police. This can reduce congestion, delay, road accidents, need for manpower. Under image processing, we use sub techniques like RGB to Gray conversion, Image resizing, Image Enhancement, Edge detection, Image matching, and Timing allocation. A real-time image is captured for every 1 second. After edge detection procedure for both reference and real-time images, these images are compared using SURF Algorithm. Then the amount of traffic is detected and the details are stored in the server. Arduino is used for a traffic signal in the hardware part. It consists of a Wi-Fi module. The micro-controller used in the system Arduino. Four cameras are placed on respective roads and these cameras are used to capture images to analyze traffic density. Then the traffic signals are decided according to the density of traffic. Our technique can be effective to combat traffic on Indian Roads. A lot of time can be saved by deploying this system and also it conserves a lot of resources as well as the economy

scholarly journals Design and Development of Traffic Control System using Image Processing

2021 ◽  
Vol 9 (VI) ◽  
pp. 3247-3252
Author(s):  
G. Kalyan
Keyword(s):  
Image Processing ◽  
Real Time ◽  
Traffic Control ◽  
Traffic Congestion ◽  
Traffic Management ◽  
Red Light ◽  
Traffic Density ◽  
The Road ◽  
Real Time Traffic ◽  
On The Road

Traffic congestion is now a big issue. Although it seems to penetrate throughout the world, urban towns are the ones which are most effected. And it is expanding in nature that it is necessary to understand the density of roads in real time to better regulate signals and efficient management of transport. Various traffic congestions, such as limited capacity, unrestricted demand, huge Red Light waits might occur. While insufficient capacity and unlimited demand are somehow interconnected, their delay in lighting is difficult to encode and not traffic dependant. The necessity to simulate and optimise traffic controls therefore arises in order to better meet this growing demand. The traffic management of information, ramp metering, and updates in real-time has been frequently used in recent years for image processing and monitoring systems. An image processing can also be used for the traffic density estimation. This research describes the approach for the computation of real-time traffic density by image processing for using live picture feed from cameras. It focuses also on the algorithm for the transmission of traffic signals on the road according to the density of vehicles and therefore aims to reduce road congestion, which reduces the number of accidents.

scholarly journals A Review on Minimization of Ambulance Response Time Using Image Processing and Critical path Mapping Based on Traffic Con-trol

2021 ◽  
Vol 2 (2) ◽  
pp. 1-7
Author(s):  
Shuja Rafiq ◽  
Keyword(s):  
Image Processing ◽  
Traffic Congestion ◽  
Traffic Management ◽  
Critical Path ◽  
Machine Learning Techniques ◽  
Emergency Vehicle ◽  
Real Time Traffic ◽  
Traffic Management System ◽  
Process Work ◽  
Economic Developments

India is a developing country; the population of India is growing exponentially. India ranks 2nd in the world in terms of population. As there will be a gradual increase in population there will be an increase in the number of vehicles, as a result of which traffic congestion is increasing and as a result, emergency vehicles such as ambulances, fire-fighters, etc. are having difficulty getting to their destination on time. Vehicle use is growing rapidly due to recent technological and economic developments, and at the same time, the lack of infrastructure against demand is leading to an increase in the number of accidents and fatalities. Minor problems in our health system have prompted us to come up with a petition to make this process work and save lives. Through book reviews and reflections, I have proposed a project in a smart traffic management system using image processing. The aim of this project is to improve simulation to determine traffic congestion, to detect a crash/accident, and to obtain an ambulance using image processing and machine learning techniques. The proposed independent work is simulated in the form of an experimental setup using Arduino and LED displays that mimic real-time traffic. These simulation results reflect the terms of the acquisition as it provides an emergency vehicle pass to catch up on peak hours.

scholarly journals Ambulance detection using image processing and neural networks

2021 ◽  
Vol 2115 (1) ◽  
pp. 012036
Author(s):  
K Agrawal ◽  
M K Nigam ◽  
S Bhattacharya ◽  
G Sumathi
Keyword(s):  
Image Processing ◽  
Traffic Congestion ◽  
Tracking System ◽  
Emergency Vehicle ◽  
Traffic Light ◽  
The Road ◽  
Detection And Tracking ◽  
Vehicle Detection And Tracking ◽  
Pass Through ◽  
Physical Address

Abstract Ambulance Detection using Image Processing and Neural Network is a vehicle detection and tracking system, which recognizes the vehicle (i.e., Ambulance in this case) amidst the traffic congestion. Due to the fact from past few years, the range of vehicles usage of the road is growing each day that results in traffic congestion, for better management of this traffic this system is useful. Traffic Congestion, as mentioned above, can be observed at an ever-growing pace in countries like India and Thailand, where the roads’ width and length make it impossible to make a separate lane for the emergency vehicle (like that of ambulance); Hence making it hard for the vehicle to pass through the traffic at the earliest possible time. The Ambulance tracking system is activated at the mapped junctions and that program detects the ambulance coming close to it and turns the traffic light to Green for the next 15 seconds. Geocoding is the practice of transforming addresses (like a physical address) to location information (like longitude and latitude) that can be used to locate a label on a map or to mark a grid. They plan to provide ambulances with this software to make it easy to transform addresses into a programmable format for review and retrieval. This data is converted to a system that shows all the crossings it must pass to meet the endpoint.

scholarly journals Efficient Traffic Management System

2018 ◽  
Vol 7 (3.12) ◽  
pp. 926
Author(s):  
Suraj Kumar G Shukla ◽  
Aadithya Kandeth ◽  
D Sai Santhiya ◽  
Kayalvizhi Jayavel
Keyword(s):  
Image Processing ◽  
Traffic Congestion ◽  
Traffic Management ◽  
Management System ◽  
Large Scale ◽  
Traffic Signals ◽  
Input Image ◽  
Traffic Density ◽  
Use Of Technology ◽  
Traffic Management System

Traffic Management is a big issue which impacts us almost daily. Use of technology such as IoT and image processing can lead to a smooth traffic management system. The common reason for traffic congestion is due to the lack of an efficient traffic prioritization system. The internet of things is a network of devices. The embedded systems includes sensors, actuators, and electronics.With software and connectivity locally or over internet  helps in  transfer of data. Each of these devices is uniquely identifiable in the network and are highly interoperable. Image processing using OpenCV is a technique that is used to process an input image and obtain the traffic densities along various lanes in a junction. Existing traffic management solutions include using RFID tags on vehicles to obtain a vehicle count. This can also be done using ultrasonic sensors. The problem with these methods is that when implemented in a large scale, the cost of the entire system can be exponentially higher than an image processing approach as each vehicle will have to be fitted with an RFID tag. Hence, implementing this model at a largescale level, for example in a metropolitan city will be time consuming and expensive. This has led to the development of our algorithm, which uses image processing and IoT along with CCTV cameras. This system is efficient, as it uses CCTV cameras that are already present in traffic signals of most major metropolitan cities. Hence implementing the system at a largescale level is feasible. This algorithm also takes care of corner cases like heavy utility vehicles and motorbikes. It can also be used at night and during unfavorable weather conditions.The algorithm used detects the density of traffic as opposed to the count of vehicles by taking input images from a CCTV camera, comparing it with a sample image of an empty road and obtaining a match percentage. The traffic density can be found easily using this since it has a disproportionate relation with the match percentage. The traffic signals can be altered accordingly using the traffic density. The output is then sent to the ThingSpeak cloud where it can be analyzed.

scholarly journals Smart Parking System: Technologies and Considerations

2020 ◽  
Vol 9 (4) ◽  
pp. 647-650
Keyword(s):  
Image Processing ◽  
Traffic Congestion ◽  
Economic Behavior ◽  
Standard Of Living ◽  
Parking Space ◽  
Parking System ◽  
Smart Parking

In today’s world with a increase in economic behavior and standard of living people who own automobiles have increased recently and this leads to a rise in jams and car/vehicle parking rose to be a major problem. Searching for a parking space will be a major challenge and due to this a lot of traffic congestion will be created. Hence the solution to these can be found out in two ways one is using the intelligent parking system with image processing and second one is smart parking system based on reservation (SPSR) both of the above mentioned technology have been implemented in different ways and henceforth will find out which of the above two technology is efficient and see to it that the best solution to the problem can be obtained

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