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المؤلفAlmaadeed, N.
المؤلفAlmaadeed, Noor
المؤلفAsim, Muhammad
المؤلفAl-Maadeed, Somaya
المؤلفBouridane, Ahmed
المؤلفBeghdadi, Azeddine
تاريخ الإتاحة2019-09-17T09:17:36Z
تاريخ النشر2018-06-06
اسم المنشورSensors (Switzerland)en_US
المعرّفhttp://dx.doi.org/10.3390/s18061858
الاقتباسAlmaadeed, N.; Asim, M.; Al-Maadeed, S.; Bouridane, A.; Beghdadi, A. Automatic Detection and Classification of Audio Events for Road Surveillance Applications. Sensors 2018, 18, 1858.
الرقم المعياري الدولي للكتاب1424-8220
معرّف المصادر الموحدhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85048310896&origin=inward
معرّف المصادر الموحدhttp://hdl.handle.net/10576/11877
الملخص© 2018 by the author. Licensee MDPI, Basel, Switzerland. This work investigates the problem of detecting hazardous events on roads by designing an audio surveillance system that automatically detects perilous situations such as car crashes and tire skidding. In recent years, research has shown several visual surveillance systems that have been proposed for road monitoring to detect accidents with an aim to improve safety procedures in emergency cases. However, the visual information alone cannot detect certain events such as car crashes and tire skidding, especially under adverse and visually cluttered weather conditions such as snowfall, rain, and fog. Consequently, the incorporation of microphones and audio event detectors based on audio processing can significantly enhance the detection accuracy of such surveillance systems. This paper proposes to combine time-domain, frequency-domain, and joint time-frequency features extracted from a class of quadratic time-frequency distributions (QTFDs) to detect events on roads through audio analysis and processing. Experiments were carried out using a publicly available dataset. The experimental results conform the effectiveness of the proposed approach for detecting hazardous events on roads as demonstrated by 7% improvement of accuracy rate when compared against methods that use individual temporal and spectral features.
راعي المشروعFunding text #1 The ability to detect hazardous events on the road can be a matter of life and death, or it can emxpeearnimthenetadtiifofne,regnenceerbateetdwteheenreasunlotsramnadlwliafes raenspdoansliibfelewfoirthwraitminagjothrehpaanpderi.caSp.A..I-Mn .thanisalwyzoerdk,thweerehsualvtse proposedasystemthatcombinest-,f-and(t,f)-domainfeaturestosimultaneouslyconsiderthenon-approach and the results to further improve the quality of the paper. stationary,instantaneous,andlong-termpropertiesofaudiosignalstofacilitateautomaticdetection and classificationofaudio anomalies. The results of experiments performed on a publiclyavailable National Research Fund (a member of the Qatar Foundation). The statements made herein are solely the rbesponsibilityenchmark dofattheaseauthors.t demonstrate the robustness of the proposed method against background noise compared with state-of-the-art approaches for road surveillance applications. Our audio classification system is confirmed to be effective indetecting hazardous...View all Funding text #2 Acknowledgments: This publication was made possible by NPRP grant # NPRP8-140-2-065 from the Qatar National Research Fund (a member of the Qatar Foundation). The statements made herein are solely the respoDatansibifrliomtyothefthFatalityeauthoAnalysisrs. Reporting System and the General Estimates System; National Highway Traffic Safety Administration: Washington, DC, USA, 2015. 5.ConfEvanco,lictsofInWt.eMre.sThet:ThImpacteauthoofrsRapiddeclareIncidentthattherDetectionearenocononfFrliceewaytsofinAccidentterest. Fatalities. Mitretek: McLean,
اللغةen
الناشرMDPI
الموضوعCar crashes
الموضوعEvent detection
الموضوعHazardous events
الموضوعTire skidding
الموضوعVisual surveillance
العنوانAutomatic detection and classification of audio events for road surveillance applications
النوعArticle
رقم العدد6
رقم المجلد18
elsevier.identifier.scopusid SCOPUS_ID:85048310896


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