Automatic Detection and Classification of Audio Events for Road Surveillance Applications
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 fo...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2018-06-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | http://www.mdpi.com/1424-8220/18/6/1858 |
| _version_ | 1811186168894586880 |
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| author | Noor Almaadeed Muhammad Asim Somaya Al-Maadeed Ahmed Bouridane Azeddine Beghdadi |
| author_facet | Noor Almaadeed Muhammad Asim Somaya Al-Maadeed Ahmed Bouridane Azeddine Beghdadi |
| author_sort | Noor Almaadeed |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-11T13:41:09Z |
| format | Article |
| id | doaj.art-c63c1c61c4804d8f9d70502ca2d659fd |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T13:41:09Z |
| publishDate | 2018-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-c63c1c61c4804d8f9d70502ca2d659fd2022-12-22T04:21:14ZengMDPI AGSensors1424-82202018-06-01186185810.3390/s18061858s18061858Automatic Detection and Classification of Audio Events for Road Surveillance ApplicationsNoor Almaadeed0Muhammad Asim1Somaya Al-Maadeed2Ahmed Bouridane3Azeddine Beghdadi4Department of Computer Science and Engineering, College of Engineering, Qatar University, Doha 2713, QatarDepartment of Computer Science and Engineering, College of Engineering, Qatar University, Doha 2713, QatarDepartment of Computer Science and Engineering, College of Engineering, Qatar University, Doha 2713, QatarDepartment of Computer and Information Sciences, Northumbria University Newcastle, Newcastle upon Tyne NE1 8ST, UKL2TI, Institut Galilée, Université Paris 13, Sorbonne Paris Cité 99, Avenue J.B. Clément, 93430 Villetaneuse, FranceThis 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.http://www.mdpi.com/1424-8220/18/6/1858event detectionvisual surveillancetire skiddingcar crasheshazardous events |
| spellingShingle | Noor Almaadeed Muhammad Asim Somaya Al-Maadeed Ahmed Bouridane Azeddine Beghdadi Automatic Detection and Classification of Audio Events for Road Surveillance Applications Sensors event detection visual surveillance tire skidding car crashes hazardous events |
| title | Automatic Detection and Classification of Audio Events for Road Surveillance Applications |
| title_full | Automatic Detection and Classification of Audio Events for Road Surveillance Applications |
| title_fullStr | Automatic Detection and Classification of Audio Events for Road Surveillance Applications |
| title_full_unstemmed | Automatic Detection and Classification of Audio Events for Road Surveillance Applications |
| title_short | Automatic Detection and Classification of Audio Events for Road Surveillance Applications |
| title_sort | automatic detection and classification of audio events for road surveillance applications |
| topic | event detection visual surveillance tire skidding car crashes hazardous events |
| url | http://www.mdpi.com/1424-8220/18/6/1858 |
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