Abstract
The development of intelligent transportation systems requires the availability of both accurate traffic information in real time and a cost-effective solution. In this paper, we describe Street Viewer, a system capable of analyzing the traffic behavior in different scenarios from images taken with an off-the-shelf optical camera. Street Viewer operates in real time on embedded hardware architectures with limited computational resources. The system features a pipelined architecture that, on one side, allows one to exploit multi-threading intensively and, on the other side, allows one to improve the overall accuracy and robustness of the system, since each layer is aimed at refining for the following layers the information it receives as input. Another relevant feature of our approach is that it is self-adaptive. During an initial setup, the application runs in learning mode to build a model of the flow patterns in the observed area. Once the model is stable, the system switches to the on-line mode where the flow model is used to count vehicles traveling on each lane and to produce a traffic information summary. If changes in the flow model are detected, the system switches back autonomously to the learning mode. The accuracy and the robustness of the system are analyzed in the paper through experimental results obtained on several different scenarios and running the system for long periods of time.
Highlights
With increasing urbanization and vehicle availability, traffic systems in urban areas encounter many challenges, such as congestion, accidents and violence [1]
Concluding, the final flow model is represented as a list of lanes, each characterized by a list of cells and a curve L(s), which will be the target of our subsequent counting activity
A video-camera of the AXIS P13 Network Camera Series [23], having a varying resolution ranging from SVGA up to 5 Mpixel, including HDTV 720 and 1080 pixel video
Summary
With increasing urbanization and vehicle availability, traffic systems in urban areas encounter many challenges, such as congestion, accidents and violence [1]. Given the costs of such solutions, research on traffic flow monitoring systems, which aim to monitor and manage traffic streams, has attracted much attention. Video cameras have a lower cost, are less invasive and can produce richer information without effecting the integrity of the road [2]. As human operators are expensive and unreliable, optimal use of videos can be made only by automated surveillance systems, adopting efficient real-time computer vision algorithms [3,4]. Processing techniques of vision-based traffic flow monitoring are usually based on reliable and robust foreground vehicle detection. Most traditional approaches fail when a car accident or a temporary road construction modifies the original car stream
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