@article{ref1,
title="Efficient violence detection in surveillance",
journal="Sensors (Basel)",
year="2022",
author="Vijeikis, Romas and Raudonis, Vidas and Dervinis, Gintaras",
volume="22",
number="6",
pages="e2216-e2216",
abstract="Intelligent video surveillance systems are rapidly being introduced to public places. The adoption of computer vision and machine learning techniques enables various applications for collected video features; one of the major is safety monitoring. The efficacy of violent event detection is measured by the efficiency and accuracy of violent event detection. In this paper, we present a novel architecture for violence detection from video surveillance cameras. Our proposed model is a spatial feature extracting a U-Net-like network that uses MobileNet V2 as an encoder followed by LSTM for temporal feature extraction and classification. The proposed model is computationally light and still achieves good results-experiments showed that an average accuracy is 0.82 ± 2% and average precision is 0.81 ± 3% using a complex real-world security camera footage dataset based on RWF-2000.<p /> <p>Language: en</p>",
language="en",
issn="1424-8220",
doi="10.3390/s22062216",
url="http://dx.doi.org/10.3390/s22062216"
}