Automatic Detection and Classification of Audio Events for Road Surveillance Applications

Almaadeed, Noor, Asim, Muhammad, Al-Maadeed, Somaya, Bouridane, Ahmed and Beghdadi, Azeddine (2018) Automatic Detection and Classification of Audio Events for Road Surveillance Applications. Sensors, 18 (6). p. 1858. ISSN 1424-8220

[img] Text (Full text)
Almadeed et al - Automatic Detection and Classification of Audio Events for Road Surveillance Applications AAM.docx - Accepted Version
Restricted to Repository staff only

Download (2MB)
[img]
Preview
Text (Full text)
Almadeed et al - Automatic Detection and Classification of Audio Events for Road Surveillance Applications OA.pdf - Published Version
Available under License Creative Commons Attribution 4.0.

Download (3MB) | Preview
Official URL: http://dx.doi.org/10.3390/s18061858

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.

Item Type: Article
Uncontrolled Keywords: event detection; visual surveillance; tire skidding; car crashes; hazardous events
Subjects: G400 Computer Science
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: Paul Burns
Date Deposited: 13 Jun 2018 10:20
Last Modified: 13 Jun 2018 10:30
URI: http://nrl.northumbria.ac.uk/id/eprint/34516

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year

View more statistics


Policies: NRL Policies | NRL University Deposit Policy | NRL Deposit Licence