The Best Model of Convolutional Neural Networks Combined with LSTM for the Detection of Interpersonal Physical Violence in Videos

Hugo Calderon-Vilca, Kent Cuadros Ramos, Elmer Diaz Quiroz, Jorge Angeles Rojas, René Calderon Vilca, Alejandro Apaza Tarqui

Producción científica: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

2 Citas (Scopus)

Resumen

Citizen insecurity is directly related to interpersonal physical violence, there are algorithms that allow detecting violence in videos; therefore, it is necessary to know which is the best model for detecting violence. For this research, three convolutional neural network models were compared: Xception, InceptionV3 and VGG16 each together with a recurrent LSTM network, to find out which of the models is the best for the detection of interpersonal violence in videos. The three models were trained using the Real Life Violence Situations dataset, then violence and non-violence were classified, as a result, the InceptionV3 model is the best model, managing to classify with an accuracy of 94% compared to the VGG16 and Xception models, which obtained 88% and 93% respectively. Therefore, we recommend the InceptionV3 model for the detection of interpersonal physical violence in citizen security videos.

Idioma originalInglés
Título de la publicación alojadaProceedings of the 29th Conference of Open Innovations Association FRUCT, FRUCT 2021
EditoresSergey Balandin, Yevgeni Koucheryavy, Tatiana Tyutina
EditorialIEEE Computer Society
Páginas81-86
Número de páginas6
ISBN (versión digital)9789526924458
DOI
EstadoPublicada - 12 may. 2021
Evento29th Conference of Open Innovations Association FRUCT, FRUCT 2021 - Virtual, Tampere, Finlandia
Duración: 12 may. 202114 may. 2021

Serie de la publicación

NombreConference of Open Innovation Association, FRUCT
Volumen2021-May
ISSN (versión impresa)2305-7254

Conferencia

Conferencia29th Conference of Open Innovations Association FRUCT, FRUCT 2021
País/TerritorioFinlandia
CiudadVirtual, Tampere
Período12/05/2114/05/21

Nota bibliográfica

Publisher Copyright:
© 2021 FRUCT.

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