Densenet 3D for Violent Action Recognition in Surveillance Video Sequences

Jorge Luis Suaña Chambi, Juan Carlos Gutiérrez Cáceres, Cesar Armando Beltrán Castañón

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

Resumen

Automatic fight detection in video sequences is an important topic for surveillance systems. The use of machine learning techniques made possible the better detection of fight,however, the models have difficulties in identifying fights in asequence of events in real time, due to the multiple degrees of freedom in the video capture such as: lighting, focus, resolution etc. Therefore, in this work, we propose a model based on the3D Densenet Convolutional Network with space-time learning features for the detection of violent actions in surveillance videos sequences. We validatedour model with four datasets, three commonly used datasets aimed at fight detection and a newdata set collected from surveillance videos. Experimentation has demonstrated that our deep learning approach can discriminate fight scenes with significantly high accuracy and it is superior than other previous studies.

Idioma originalInglés
Título de la publicación alojada2022 41st International Conference of the Chilean Computer Science Society, SCCC 2022
EditorialIEEE Computer Society
ISBN (versión digital)9781665456746
DOI
EstadoPublicada - 2022
Evento41st International Conference of the Chilean Computer Science Society, SCCC 2022 - Santiago, Chile
Duración: 21 nov. 202225 nov. 2022

Serie de la publicación

NombreProceedings - International Conference of the Chilean Computer Science Society, SCCC
Volumen2022-November
ISSN (versión impresa)1522-4902

Conferencia

Conferencia41st International Conference of the Chilean Computer Science Society, SCCC 2022
País/TerritorioChile
CiudadSantiago
Período21/11/2225/11/22

Nota bibliográfica

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© 2022 IEEE.

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