Car crashes detection by audio analysis in crowded roads

Pasquale Foggia, Alessia Saggese, Nicola Strisciuglio, Mario Vento, Nicolai Petkov

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

9 Citations (Scopus)

Abstract

In the last years, video surveillance has been employed for roads monitoring in order to detect abnormal events and improve the safety procedures in case of emergency. Certain events, such as car crashes or tire skidding, are difficult or impossible to detect when only the visual information is considered. In this paper we describe a preliminary system to detect events in roads by means of audio analysis. The system that we propose combines short- and long-time analysis of the audio signal in order to detect both impulsive and sustained events. We present the preliminary results achieved by the proposed system on a data set specifically made for roads surveillance, which we made publicly available. We also discuss the architectural deployment of such system in real environments with respect to a model of the noise of road traffic. The achieved results are promising and confirm the effectiveness of the system.

Original languageEnglish
Title of host publication2015 12th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS 2015)
Place of PublicationPiscataway, NJ
PublisherIEEE
ISBN (Electronic)978-1-4673-7631-0
ISBN (Print)978-1-4673-7632-7
DOIs
Publication statusPublished - 19 Oct 2015
Externally publishedYes
Event12th IEEE International Conference on Advanced Video and Signal Based Surveillance, AVSS 2015 - Karlsruhe, Germany
Duration: 25 Aug 201528 Aug 2015
Conference number: 12

Conference

Conference12th IEEE International Conference on Advanced Video and Signal Based Surveillance, AVSS 2015
Abbreviated titleAVSS 2015
CountryGermany
CityKarlsruhe
Period25/08/1528/08/15

Keywords

  • Roads
  • Signal to noise ratio
  • Support vector machines
  • Surveillance
  • Tires
  • Vehicle crash testing
  • Vehicles

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