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

Facultad de Ingeniería

Universidad Peruana de Ciencias Aplicadas

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución a la conferencia › revisión exhaustiva

Resumen

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.

Idioma original	Inglés
Título de la publicación alojada	Information Management and Big Data - 7th Annual International Conference, SIMBig 2020, Proceedings
Editores	Juan Antonio Lossio-Ventura, Jorge Carlos Valverde-Rebaza, Eduardo Díaz, Hugo Alatrista-Salas
Editorial	Springer Science and Business Media Deutschland GmbH
Páginas	279-290
Número de páginas	12
ISBN (versión impresa)	9783030762278
DOI	https://doi.org/10.1007/978-3-030-76228-5_20
Estado	Publicada - 2021
Evento	7th Annual International Conference on Information Management and Big Data, SIMBig 2020 - Virtual, Online Duración: 1 oct. 2020 → 3 oct. 2020

Serie de la publicación

Nombre	Communications in Computer and Information Science
Volumen	1410 CCIS
ISSN (versión impresa)	1865-0929
ISSN (versión digital)	1865-0937

Conferencia

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

ODS de las Naciones Unidas

Este resultado contribuye a los siguientes Objetivos de Desarrollo Sostenible

Acceder al documento

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

Otros archivos y enlaces

Enlace a la publicación en Scopus

Citar esto

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. En 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).

@inproceedings{f7f7e12ad4a3422f92762b3663fc1889,

title = "Static Summarization Using Pearson{\textquoteright}s Coefficient and Transfer Learning for Anomaly Detection for Surveillance Videos",

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",

author = "Chancolla-Neira, \{Steve Willian\} and Salinas-Lozano, \{C{\'e}sar Ernesto\} and Willy Ugarte",

note = "Publisher Copyright: {\textcopyright} 2021, Springer Nature Switzerland AG.; 7th Annual International Conference on Information Management and Big Data, SIMBig 2020 ; Conference date: 01-10-2020 Through 03-10-2020",

year = "2021",

doi = "10.1007/978-3-030-76228-5\_20",

language = "Ingl{\'e}s",

isbn = "9783030762278",

series = "Communications in Computer and Information Science",

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "279--290",

editor = "Lossio-Ventura, \{Juan Antonio\} and Valverde-Rebaza, \{Jorge Carlos\} and Eduardo D{\'i}az and Hugo Alatrista-Salas",

booktitle = "Information Management and Big Data - 7th Annual International Conference, SIMBig 2020, Proceedings",

}

Chancolla-Neira, SW, Salinas-Lozano, CE & Ugarte, W 2021, Static Summarization Using Pearson’s Coefficient and Transfer Learning for Anomaly Detection for Surveillance Videos. En 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).

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución a la conferencia › revisión exhaustiva

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. En Lossio-Ventura JA, Valverde-Rebaza JC, Díaz E, Alatrista-Salas H, editores, 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

Resumen

Serie de la publicación

Conferencia

ODS de las Naciones Unidas

Acceder al documento

Otros archivos y enlaces

Huella

Citar esto