Detecting Sounds of Interest in Roads with Deep Networks

Pasquale Foggia, Alessia Saggese*, Nicola Strisciuglio, Mario Vento, Vincenzo Vigilante

*Corresponding author for this work

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

6 Citations (Scopus)
45 Downloads (Pure)


Monitoring of public and private places is of great importance for security of people and is usually done by means of surveillance cameras. In this paper we propose an approach for monitoring of roads, to detect car crashes and tire skidding, based on the analysis of sound signals, which can complement or, in some cases, substitute video analytic systems. The system that we propose employs a MobileNet deep architecture, designed to efficiently run on embedded appliances and be deployed on distributed systems for road monitoring. We designed a recognition system based on analysis of audio frames and tested it on the publicly available MIVIA road events data set. The performance results that we achieved (recognition rate higher than 99%) are higher than existing methods, demonstrating that the proposed approach can be deployed on embedded devices in a distributed surveillance system.

Original languageEnglish
Title of host publicationImage Analysis and Processing – ICIAP 2019 - 20th International Conference, Proceedings
EditorsElisa Ricci, Nicu Sebe, Samuel Rota Bulò, Cees Snoek, Oswald Lanz, Stefano Messelodi
PublisherSpringer Verlag
Number of pages10
ISBN (Electronic)978-3-030-30645-8
ISBN (Print)978-3-030-30644-1
Publication statusPublished - Sep-2019
Event20th International Conference on Image Analysis and Processing, ICIAP 2019 - Trento, Italy
Duration: 9-Sep-201913-Sep-2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference20th International Conference on Image Analysis and Processing, ICIAP 2019


  • Audio event detection
  • Deep learning
  • MobileNet

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