Automatic Detection and Classification of Audio Events for Road Surveillance Applications
1
Department of Computer Science and Engineering, College of Engineering, Qatar University, Doha 2713, Qatar
2
Department of Computer and Information Sciences, Northumbria University Newcastle, Newcastle upon Tyne NE1 8ST, UK
3
L2TI, Institut Galilée, Université Paris 13, Sorbonne Paris Cité 99, Avenue J.B. Clément, 93430 Villetaneuse, France
*
Author to whom correspondence should be addressed.
Sensors 2018, 18(6), 1858; https://doi.org/10.3390/s18061858
Received: 23 March 2018 / Revised: 17 April 2018 / Accepted: 24 April 2018 / Published: 6 June 2018
(This article belongs to the Special Issue Sensor Signal and Information Processing)
Abstract
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. View Full-Text
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MDPI and ACS Style
Almaadeed, 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.
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