@article{ref1,
title="A controlled benchmark of video violence detection techniques",
journal="Information (Basel)",
year="2020",
author="Convertini, Nicola and Dentamaro, Vincenzo and Impedovo, Donato and Pirlo, Giuseppe and Sarcinella, Lucia",
volume="11",
number="6",
pages="e321-e321",
abstract="This benchmarking study aims to examine and discuss the current state-of-the-art techniques for in-video violence detection, and also provide benchmarking results as a reference for the future accuracy baseline of violence detection systems. In this paper, the authors review 11 techniques for in-video violence detection. They re-implement five carefully chosen state-of-the-art techniques over three different and publicly available violence datasets, using several classifiers, all in the same conditions. The main contribution of this work is to compare feature-based violence detection techniques and modern deep-learning techniques, such as Inception V3.<p /> <p>Language: en</p>",
language="en",
issn="2078-2489",
doi="10.3390/info11060321",
url="http://dx.doi.org/10.3390/info11060321"
}