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Automatic Detection and Classification of Audio Events for Road Surveillance Applications
- Source :
- Sensors (Basel, Switzerland), Sensors, Volume 18, Issue 6, Sensors, Vol 18, Iss 6, p 1858 (2018)
- Publication Year :
- 2018
- Publisher :
- MDPI, 2018.
-
Abstract
- © 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. Funding 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
- Subjects :
- Computer science
Real-time computing
02 engineering and technology
lcsh:Chemical technology
computer.software_genre
Biochemistry
Article
Analytical Chemistry
Visual surveillance
0202 electrical engineering, electronic engineering, information engineering
Tire skidding
tire skidding
car crashes
lcsh:TP1-1185
Hazardous events
Electrical and Electronic Engineering
Audio signal processing
Instrumentation
visual surveillance
Car crashes
Event (computing)
G400
Detector
020206 networking & telecommunications
Atomic and Molecular Physics, and Optics
event detection
Audio analyzer
020201 artificial intelligence & image processing
Event detection
Joint (audio engineering)
computer
hazardous events
Subjects
Details
- Language :
- English
- ISSN :
- 14248220
- Volume :
- 18
- Issue :
- 6
- Database :
- OpenAIRE
- Journal :
- Sensors (Basel, Switzerland)
- Accession number :
- edsair.doi.dedup.....b6e48548543caa38f7915a926da5e871