Detecting sounds of interest in roads with deep networks

Pasquale Foggia; Alessia Saggese; Nicola Strisciuglio; Mario Vento; Vincenzo Vigilante

doi:10.1007/978-3-030-30645-8_53

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 proceeding › Conference contribution › Academic › peer-review

11 Citations (Scopus)

Abstract

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 language	English
Title of host publication	Image Analysis and Processing – ICIAP 2019
Subtitle of host publication	20th International Conference
Editors	Elisa Ricci, Samuel Rota Bulò, Cees Snoek, Oswald Lanz, Stefano Messelodi, Nicu Sebe
Publisher	Springer
Pages	583-592
Number of pages	10
ISBN (Electronic)	978-3-030-30645-8
ISBN (Print)	978-3-030-30644-1
DOIs	https://doi.org/10.1007/978-3-030-30645-8_53
Publication status	Published - 2 Sept 2019
Externally published	Yes
Event	20th International Conference on Image Analysis and Processing, ICIAP 2019 - Trento, Italy Duration: 9 Sept 2019 → 13 Sept 2019 Conference number: 20

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11752 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	20th International Conference on Image Analysis and Processing, ICIAP 2019
Abbreviated title	ICIAP 2019
Country/Territory	Italy
City	Trento
Period	9/09/19 → 13/09/19

Keywords

Audio event detection
Deep learning
MobileNet
n/a OA procedure

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1007/978-3-030-30645-8_53

Cite this

Foggia, P., Saggese, A., Strisciuglio, N., Vento, M., & Vigilante, V. (2019). Detecting sounds of interest in roads with deep networks. In E. Ricci, S. Rota Bulò, C. Snoek, O. Lanz, S. Messelodi, & N. Sebe (Eds.), Image Analysis and Processing – ICIAP 2019: 20th International Conference (pp. 583-592). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11752 LNCS). Springer. https://doi.org/10.1007/978-3-030-30645-8_53

Foggia, Pasquale ; Saggese, Alessia ; Strisciuglio, Nicola et al. / Detecting sounds of interest in roads with deep networks. Image Analysis and Processing – ICIAP 2019: 20th International Conference. editor / Elisa Ricci ; Samuel Rota Bulò ; Cees Snoek ; Oswald Lanz ; Stefano Messelodi ; Nicu Sebe. Springer, 2019. pp. 583-592 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{730bef769e334c39add803bfc291b82f,

title = "Detecting sounds of interest in roads with deep networks",

abstract = "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.",

keywords = "Audio event detection, Deep learning, MobileNet, n/a OA procedure",

author = "Pasquale Foggia and Alessia Saggese and Nicola Strisciuglio and Mario Vento and Vincenzo Vigilante",

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Foggia, P, Saggese, A, Strisciuglio, N, Vento, M & Vigilante, V 2019, Detecting sounds of interest in roads with deep networks. in E Ricci, S Rota Bulò, C Snoek, O Lanz, S Messelodi & N Sebe (eds), Image Analysis and Processing – ICIAP 2019: 20th International Conference. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11752 LNCS, Springer, pp. 583-592, 20th International Conference on Image Analysis and Processing, ICIAP 2019, Trento, Italy, 9/09/19. https://doi.org/10.1007/978-3-030-30645-8_53

Detecting sounds of interest in roads with deep networks. / Foggia, Pasquale; Saggese, Alessia; Strisciuglio, Nicola et al.
Image Analysis and Processing – ICIAP 2019: 20th International Conference. ed. / Elisa Ricci; Samuel Rota Bulò; Cees Snoek; Oswald Lanz; Stefano Messelodi; Nicu Sebe. Springer, 2019. p. 583-592 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11752 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

TY - GEN

T1 - Detecting sounds of interest in roads with deep networks

AU - Foggia, Pasquale

AU - Saggese, Alessia

AU - Strisciuglio, Nicola

AU - Vento, Mario

AU - Vigilante, Vincenzo

N1 - Conference code: 20

PY - 2019/9/2

Y1 - 2019/9/2

N2 - 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.

AB - 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.

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SN - 978-3-030-30644-1

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 583

EP - 592

BT - Image Analysis and Processing – ICIAP 2019

A2 - Ricci, Elisa

A2 - Rota Bulò, Samuel

A2 - Snoek, Cees

A2 - Lanz, Oswald

A2 - Messelodi, Stefano

A2 - Sebe, Nicu

PB - Springer

T2 - 20th International Conference on Image Analysis and Processing, ICIAP 2019

Y2 - 9 September 2019 through 13 September 2019

ER -

Foggia P, Saggese A, Strisciuglio N, Vento M, Vigilante V. Detecting sounds of interest in roads with deep networks. In Ricci E, Rota Bulò S, Snoek C, Lanz O, Messelodi S, Sebe N, editors, Image Analysis and Processing – ICIAP 2019: 20th International Conference. Springer. 2019. p. 583-592. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-30645-8_53

Detecting sounds of interest in roads with deep networks

Abstract

Publication series

Conference

Keywords

UN SDGs

Access to Document

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