Evaluation of the Traffic Parameters in a Metropolitan Area by Fusing Visual Perceptions and CNN Processing of Webcam Images

Abstract

This paper proposes a traffic monitoring architecture based on a high-speed communication network whose nodes are equipped with fuzzy processors and cellular neural network (CNN) embedded systems. It implements a real-time mobility information system where visual human perceptions sent by people working on the territory and video-sequences of traffic taken from webcams are jointly processed to evaluate the fundamental traffic parameters for every street of a metropolitan area. This paper presents the whole methodology for data collection and analysis and compares the accuracy and the processing time of the proposed soft computing techniques with other existing algorithms. Moreover, this paper discusses when and why it is recommended to fuse the visual perceptions of the traffic with the automated measurements taken from the webcams to compute the maximum traveling time that is likely needed to reach any destination in the traffic network.

An Intelligent Video Surveillance Framework with Big Data Management for Indian Road Traffic System

“…However, the determination of grid size, position and orientation is rather arbitrary. It also fails to provide information within grids, and may bring obstacles to road selection process, since grid boundaries may 'split' the roads into several parts and ultimately give rise to the loss of information about connectivity [5,4].…”

“…Mesh density based on sub-region avoids several of the aforementioned setbacks. However, it neglects geographical characteristics of road networks and may not reflect information about the area each road is serving and its relative importance [5]. The problem of road monitoring as it is chosen in our research is presented as a sequence of independent processing steps intended to solve tasks logically connected to each other.…”

“…Despite all these things the leverage of software systems for traffic management has emerged. But the focus is narrowed down to develop a core video processing algorithm, which serves the purpose of traffic monitoring [4,5]. The question comes in our mind, whether the core video processing algorithm is applicable for large scale video analytics when the input data is available in cloud storage?…”

An arduino based system provided with GPS/GPRS shield for real time monitoring of traffic flows

“…MONITORING TRAFFIC FLOWS FROM GPS SIGNALS As known, traffic flows may be measured by monitoring the car flows using pneumatic tubes or magnetic loops [4], traffic videos [5] even in environments with little luminosity [6] or affected by noisy conditions [7], from traffic policemen perceptions [8], and with the GPS Signals from the mobile of the users [9]. This section points out how an Arduino System provided with a GPS shield may be used for driver tracking with a method that is simpler than the ones proposed in literature, e.g., [9], but fast enough to allow the server to calculate the travel time for each road section in real time.…”

Location based services for mobile users: a scenario based implementation account

“…Concerning the parameters affecting the individual terms (e.g., travel time, vacancies, and so on) they should be defined on the basis of the available sensing technologies, e.g., [16], [17], or from the available business information.…”