Static Summarization Using Pearson’s Coefficient and Transfer Learning for Anomaly Detection for Surveillance Videos

Steve Willian Chancolla-Neira; César Ernesto Salinas-Lozano; Willy Ugarte

doi:10.1007/978-3-030-76228-5_20

Static Summarization Using Pearson’s Coefficient and Transfer Learning for Anomaly Detection for Surveillance Videos

Steve Willian Chancolla-Neira
, César Ernesto Salinas-Lozano
, Willy Ugarte

Faculty of Engineering

Universidad Peruana de Ciencias Aplicadas

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

Abstract

Data storage has been a problem as technology advances, there are more devices capable of capturing images, sounds, videos, etc. On the security side, many people choose to use security cameras that are available 24 h a day to capture anomalous events and maintain the security of the area, however, storing all captured videos generates high costs, as well as the prolonged analysis that this type of videos implies. For this reason, we propose a method that allows selecting only the important events captured by a video surveillance camera and then classifying them among the types of most constant criminal acts in Peru.

Original language	English
Title of host publication	Information Management and Big Data - 7th Annual International Conference, SIMBig 2020, Proceedings
Editors	Juan Antonio Lossio-Ventura, Jorge Carlos Valverde-Rebaza, Eduardo Díaz, Hugo Alatrista-Salas
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	279-290
Number of pages	12
ISBN (Print)	9783030762278
DOIs	https://doi.org/10.1007/978-3-030-76228-5_20
State	Published - 2021
Event	7th Annual International Conference on Information Management and Big Data, SIMBig 2020 - Virtual, Online Duration: 1 Oct 2020 → 3 Oct 2020

Publication series

Name	Communications in Computer and Information Science
Volume	1410 CCIS
ISSN (Print)	1865-0929
ISSN (Electronic)	1865-0937

Conference

Conference	7th Annual International Conference on Information Management and Big Data, SIMBig 2020
City	Virtual, Online
Period	1/10/20 → 3/10/20

UN SDGs

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

SDG 16 Peace, Justice and Strong Institutions

Keywords

Convolutional neuronal networks
Image processing
Summarization
Surveillance videos
Video classification

Access to Document

10.1007/978-3-030-76228-5_20

Cite this

Chancolla-Neira, S. W., Salinas-Lozano, C. E., & Ugarte, W. (2021). Static Summarization Using Pearson’s Coefficient and Transfer Learning for Anomaly Detection for Surveillance Videos. In J. A. Lossio-Ventura, J. C. Valverde-Rebaza, E. Díaz, & H. Alatrista-Salas (Eds.), Information Management and Big Data - 7th Annual International Conference, SIMBig 2020, Proceedings (pp. 279-290). (Communications in Computer and Information Science; Vol. 1410 CCIS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-76228-5_20

Chancolla-Neira, Steve Willian ; Salinas-Lozano, César Ernesto ; Ugarte, Willy. / Static Summarization Using Pearson’s Coefficient and Transfer Learning for Anomaly Detection for Surveillance Videos. Information Management and Big Data - 7th Annual International Conference, SIMBig 2020, Proceedings. editor / Juan Antonio Lossio-Ventura ; Jorge Carlos Valverde-Rebaza ; Eduardo Díaz ; Hugo Alatrista-Salas. Springer Science and Business Media Deutschland GmbH, 2021. pp. 279-290 (Communications in Computer and Information Science).

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abstract = "Data storage has been a problem as technology advances, there are more devices capable of capturing images, sounds, videos, etc. On the security side, many people choose to use security cameras that are available 24 h a day to capture anomalous events and maintain the security of the area, however, storing all captured videos generates high costs, as well as the prolonged analysis that this type of videos implies. For this reason, we propose a method that allows selecting only the important events captured by a video surveillance camera and then classifying them among the types of most constant criminal acts in Peru.",

keywords = "Convolutional neuronal networks, Image processing, Summarization, Surveillance videos, Video classification",

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Chancolla-Neira, SW, Salinas-Lozano, CE & Ugarte, W 2021, Static Summarization Using Pearson’s Coefficient and Transfer Learning for Anomaly Detection for Surveillance Videos. in JA Lossio-Ventura, JC Valverde-Rebaza, E Díaz & H Alatrista-Salas (eds), Information Management and Big Data - 7th Annual International Conference, SIMBig 2020, Proceedings. Communications in Computer and Information Science, vol. 1410 CCIS, Springer Science and Business Media Deutschland GmbH, pp. 279-290, 7th Annual International Conference on Information Management and Big Data, SIMBig 2020, Virtual, Online, 1/10/20. https://doi.org/10.1007/978-3-030-76228-5_20

Static Summarization Using Pearson’s Coefficient and Transfer Learning for Anomaly Detection for Surveillance Videos. / Chancolla-Neira, Steve Willian; Salinas-Lozano, César Ernesto; Ugarte, Willy.
Information Management and Big Data - 7th Annual International Conference, SIMBig 2020, Proceedings. ed. / Juan Antonio Lossio-Ventura; Jorge Carlos Valverde-Rebaza; Eduardo Díaz; Hugo Alatrista-Salas. Springer Science and Business Media Deutschland GmbH, 2021. p. 279-290 (Communications in Computer and Information Science; Vol. 1410 CCIS).

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

TY - GEN

T1 - Static Summarization Using Pearson’s Coefficient and Transfer Learning for Anomaly Detection for Surveillance Videos

AU - Chancolla-Neira, Steve Willian

AU - Salinas-Lozano, César Ernesto

AU - Ugarte, Willy

PY - 2021

Y1 - 2021

N2 - Data storage has been a problem as technology advances, there are more devices capable of capturing images, sounds, videos, etc. On the security side, many people choose to use security cameras that are available 24 h a day to capture anomalous events and maintain the security of the area, however, storing all captured videos generates high costs, as well as the prolonged analysis that this type of videos implies. For this reason, we propose a method that allows selecting only the important events captured by a video surveillance camera and then classifying them among the types of most constant criminal acts in Peru.

AB - Data storage has been a problem as technology advances, there are more devices capable of capturing images, sounds, videos, etc. On the security side, many people choose to use security cameras that are available 24 h a day to capture anomalous events and maintain the security of the area, however, storing all captured videos generates high costs, as well as the prolonged analysis that this type of videos implies. For this reason, we propose a method that allows selecting only the important events captured by a video surveillance camera and then classifying them among the types of most constant criminal acts in Peru.

KW - Convolutional neuronal networks

KW - Image processing

KW - Summarization

KW - Surveillance videos

KW - Video classification

UR - https://www.scopus.com/pages/publications/85111162793

U2 - 10.1007/978-3-030-76228-5_20

DO - 10.1007/978-3-030-76228-5_20

M3 - Contribución a la conferencia

AN - SCOPUS:85111162793

SN - 9783030762278

T3 - Communications in Computer and Information Science

SP - 279

EP - 290

BT - Information Management and Big Data - 7th Annual International Conference, SIMBig 2020, Proceedings

A2 - Lossio-Ventura, Juan Antonio

A2 - Valverde-Rebaza, Jorge Carlos

A2 - Díaz, Eduardo

A2 - Alatrista-Salas, Hugo

PB - Springer Science and Business Media Deutschland GmbH

T2 - 7th Annual International Conference on Information Management and Big Data, SIMBig 2020

Y2 - 1 October 2020 through 3 October 2020

ER -

Chancolla-Neira SW, Salinas-Lozano CE, Ugarte W. Static Summarization Using Pearson’s Coefficient and Transfer Learning for Anomaly Detection for Surveillance Videos. In Lossio-Ventura JA, Valverde-Rebaza JC, Díaz E, Alatrista-Salas H, editors, Information Management and Big Data - 7th Annual International Conference, SIMBig 2020, Proceedings. Springer Science and Business Media Deutschland GmbH. 2021. p. 279-290. (Communications in Computer and Information Science). doi: 10.1007/978-3-030-76228-5_20

Static Summarization Using Pearson’s Coefficient and Transfer Learning for Anomaly Detection for Surveillance Videos

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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