23,154 research outputs found
Road Vehicle Monitoring System Based on Intelligent Visual Internet of Things
In recent years, with the rapid development of video surveillance infrastructure, more and more intelligent surveillance systems have employed computer vision and pattern recognition techniques. In this paper, we present a novel intelligent surveillance system used for the management of road vehicles based on Intelligent Visual Internet of Things (IVIoT). The system has the ability to extract the vehicle visual tags on the urban roads; in other words, it can label any vehicle by means of computer vision and therefore can easily recognize vehicles with visual tags. The nodes designed in the system can be installed not only on the urban roads for providing basic information but also on the mobile sensing vehicles for providing mobility support and improving sensing coverage. Visual tags mentioned in this paper consist of license plate number, vehicle color, and vehicle type and have several additional properties, such as passing spot and passing moment. Moreover, we present a fast and efficient image haze removal method to deal with haze weather condition. The experiment results show that the designed road vehicle monitoring system achieves an average real-time tracking accuracy of 85.80% under different conditions
A Radio-fingerprinting-based Vehicle Classification System for Intelligent Traffic Control in Smart Cities
The measurement and provision of precise and upto-date traffic-related key
performance indicators is a key element and crucial factor for intelligent
traffic controls systems in upcoming smart cities. The street network is
considered as a highly-dynamic Cyber Physical System (CPS) where measured
information forms the foundation for dynamic control methods aiming to optimize
the overall system state. Apart from global system parameters like traffic flow
and density, specific data such as velocity of individual vehicles as well as
vehicle type information can be leveraged for highly sophisticated traffic
control methods like dynamic type-specific lane assignments. Consequently,
solutions for acquiring these kinds of information are required and have to
comply with strict requirements ranging from accuracy over cost-efficiency to
privacy preservation. In this paper, we present a system for classifying
vehicles based on their radio-fingerprint. In contrast to other approaches, the
proposed system is able to provide real-time capable and precise vehicle
classification as well as cost-efficient installation and maintenance, privacy
preservation and weather independence. The system performance in terms of
accuracy and resource-efficiency is evaluated in the field using comprehensive
measurements. Using a machine learning based approach, the resulting success
ratio for classifying cars and trucks is above 99%
VANET Applications: Hot Use Cases
Current challenges of car manufacturers are to make roads safe, to achieve
free flowing traffic with few congestions, and to reduce pollution by an
effective fuel use. To reach these goals, many improvements are performed
in-car, but more and more approaches rely on connected cars with communication
capabilities between cars, with an infrastructure, or with IoT devices.
Monitoring and coordinating vehicles allow then to compute intelligent ways of
transportation. Connected cars have introduced a new way of thinking cars - not
only as a mean for a driver to go from A to B, but as smart cars - a user
extension like the smartphone today. In this report, we introduce concepts and
specific vocabulary in order to classify current innovations or ideas on the
emerging topic of smart car. We present a graphical categorization showing this
evolution in function of the societal evolution. Different perspectives are
adopted: a vehicle-centric view, a vehicle-network view, and a user-centric
view; described by simple and complex use-cases and illustrated by a list of
emerging and current projects from the academic and industrial worlds. We
identified an empty space in innovation between the user and his car:
paradoxically even if they are both in interaction, they are separated through
different application uses. Future challenge is to interlace social concerns of
the user within an intelligent and efficient driving
Sharing Human-Generated Observations by Integrating HMI and the Semantic Sensor Web
Current âInternet of Thingsâ concepts point to a future where connected objects gather meaningful information about their environment and share it with other objects and people. In particular, objects embedding Human Machine Interaction (HMI), such as mobile devices and, increasingly, connected vehicles, home appliances, urban interactive infrastructures, etc., may not only be conceived as sources of sensor information, but, through interaction with their users, they can also produce highly valuable context-aware human-generated observations. We believe that the great promise offered by combining and sharing all of the different sources of information available can be realized through the integration of HMI and Semantic Sensor Web technologies. This paper presents a technological framework that harmonizes two of the most influential HMI and Sensor Web initiatives: the W3Câs Multimodal Architecture and Interfaces (MMI) and the Open Geospatial Consortium (OGC) Sensor Web Enablement (SWE) with its semantic extension, respectively. Although the proposed framework is general enough to be applied in a variety of connected objects integrating HMI, a particular development is presented for a connected car scenario where driversâ observations about the traffic or their environment are shared across the Semantic Sensor Web. For implementation and evaluation purposes an on-board OSGi (Open Services Gateway Initiative) architecture was built, integrating several available HMI, Sensor Web and Semantic Web technologies. A technical performance test and a conceptual validation of the scenario with potential users are reported, with results suggesting the approach is soun
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