Real Time Traffic Density Count using Image Processing
TL;DR: This paper presents the algorithm to determine the number of vehicles on the road 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).
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Abstract: 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. This paper presents 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.
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Citations
Smart Control of Traffic Signal System using Image Processing
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Review on techniques for traffic jam detection and congestion avoidance
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- 18 Jun 2015
TL;DR: A GPS based system can be a better alternative technique for traffic jam detection as it can monitor the whole road network and require low installation cost and can be incorporated with the strategies for congestion avoidance which will help to improve the traffic flow.
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Traffic Analytics With Low-Frame-Rate Videos
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Automatic vehicle counting using background subtraction method on gray scale images and morphology operation
Kusworo Adi,Achmad Widodo,Catur Edi Widodo,Adi Pamungkas,Ari Bawono Putranto +4 more
- 01 May 2018
TL;DR: The best vehicle counting results were obtained in the morning with a counting accuracy of 86.36 %, whereas the lowest accuracy was in the evening, at 21.43 %.
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Agent Based Intelligent Traffic Management System for Smart Cities
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TL;DR: In this paper, a comprehensive overview of proposed and implemented control strategies is provided for three areas: urban road networks, freeway networks, and route guidance, and selected application results are briefly outlined to illustrate the impact of various control actions and strategies.
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Tommy Gärling,Geertje Schuitema +1 more
TL;DR: In this paper, the authors present a review of the effectiveness of non-coercive travel demand management (TDM) measures in reducing car use and conclude that coercive TDM measures are likely to become more effective, acceptable, and politically feasible.
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Real-time estimation of vehicle-count within signalized links
TL;DR: Simulation investigations indicate a robust estimation performance with low calibration effort needed, which facilitates easy applicability of the Kalman-Filter method.
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Automatic Traffic Density Estimation and Vehicle Classification for Traffic Surveillance Systems Using Neural Networks
Celil Ozkurt,Fatih Camci +1 more
TL;DR: This paper presents vehicle classification and traffic density calculation methods using neural networks and reports results from real traffic videos obtained from Istanbul Traffic Management Company (ISBAK).
Application of Kalman Filtering to the Surveillance and Control of Traffic Systems
Michael W. Szeto,Denos C. Gazis +1 more
TL;DR: The methodology of the discrete-time, extended Kalman filter is applied for the estimation of densities and the control of critical traffic links using traffic data obtained at the Lincoln tunnel of New York City.
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