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dc.contributor.authorOdat, Enas M.
dc.contributor.authorShamma, Jeff S.
dc.contributor.authorClaudel, Christian
dc.date.accessioned2017-10-03T12:49:39Z
dc.date.available2017-10-03T12:49:39Z
dc.date.issued2017-09-18
dc.identifier.citationOdat E, Shamma JS, Claudel C (2017) Vehicle Classification and Speed Estimation Using Combined Passive Infrared/Ultrasonic Sensors. IEEE Transactions on Intelligent Transportation Systems: 1–14. Available: http://dx.doi.org/10.1109/TITS.2017.2727224.
dc.identifier.issn1524-9050
dc.identifier.issn1558-0016
dc.identifier.doi10.1109/TITS.2017.2727224
dc.identifier.urihttp://hdl.handle.net/10754/625775
dc.description.abstractIn this paper, a new sensing device that can simultaneously monitor traffic congestion and urban flash floods is presented. This sensing device is based on the combination of passive infrared sensors (PIRs) and ultrasonic rangefinder, and is used for real-time vehicle detection, classification, and speed estimation in the context of wireless sensor networks. This framework relies on dynamic Bayesian Networks to fuse heterogeneous data both spatially and temporally for vehicle detection. To estimate the speed of the incoming vehicles, we first use cross correlation and wavelet transform-based methods to estimate the time delay between the signals of different sensors. We then propose a calibration and self-correction model based on Bayesian Networks to make a joint inference by all sensors about the speed and the length of the detected vehicle. Furthermore, we use the measurements of the ultrasonic and the PIR sensors to perform vehicle classification. Validation data (using an experimental dual infrared and ultrasonic traffic sensor) show a 99% accuracy in vehicle detection, a mean error of 5 kph in vehicle speed estimation, a mean error of 0.7m in vehicle length estimation, and a high accuracy in vehicle classification. Finally, we discuss the computational performance of the algorithm, and show that this framework can be implemented on low-power computational devices within a wireless sensor network setting. Such decentralized processing greatly improves the energy consumption of the system and minimizes bandwidth usage.
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.relation.urlhttp://ieeexplore.ieee.org/document/8039243/
dc.subjectAcoustics
dc.subjectEstimation
dc.subjectFeature extraction
dc.subjectSensor phenomena and characterization
dc.subjectSensor systems
dc.subjectWireless sensor networks
dc.titleVehicle Classification and Speed Estimation Using Combined Passive Infrared/Ultrasonic Sensors
dc.typeArticle
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentElectrical Engineering Program
dc.identifier.journalIEEE Transactions on Intelligent Transportation Systems
dc.contributor.institutionThe University of Texas at Austin, Austin, TX 78712 USA.
kaust.personOdat, Enas M.
kaust.personShamma, Jeff S.
dc.date.published-online2017-09-18
dc.date.published-print2018-05


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