Please use this identifier to cite or link to this item: http://repositorio.unicamp.br/jspui/handle/REPOSIP/342027
Type: Artigo
Title: Toward subjective violence detection in videos
Author: Peixoto, Bruno
Lavi, Bahram
Martin, João Paulo Pereira
Avila, Sandra
Dias, Zanoni
Rocha, Anderson
Abstract: Violence detection in videos aims to identify whether a violent action occurred within a video stream. Effective tools for intelligent video analysis are highly demanded, specially to determine violence in video streams. Such solution could have applications in detecting inappropriate behaviors in video feeds, aiding law-enforcement in forensic cases, protecting children from accessing inappropriate online content and helping parents making informed decisions about what their kids should watch. Prior art on violence detection, particularly recently proposed deep learning based ones, seeks to identify violence in videos as a whole, without considering breaking down the subject into some of its underlying concepts. In this paper, we explore a different methodology of violence detection, which relies upon two deep neural network (DNNs) frameworks to learn spatial-temporal information on video clips under different scenarios - subjective- and conceptual-based. We leverage deep feature representations for each specific concept, and aggregate them by training a shallow neural network as a binary-classification problem to describe violence as a whole. Finally, we show that using more specific concepts is an intuitive and effective solution, besides being complementary to form a more robust definition of violence
Subject: Computação forense
Visão por computador
Aprendizado de máquina
Semântica
Country: Estados Unidos
Editor: Institute of Electrical and Electronics Engineers
Rights: Fechado
Identifier DOI: 10.1109/ICASSP.2019.8682833
Address: https://ieeexplore.ieee.org/document/8682833
Date Issue: 2019
Appears in Collections:IC - Artigos e Outros Documentos

Files in This Item:
File Description SizeFormat 
2-s2.0-85068977309.pdf530.31 kBAdobe PDFView/Open


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.