@article{ref1,
title="Fusion object detection and action recognition to predict violent action",
journal="Sensors (Basel)",
year="2023",
author="Rodrigues, Nelson R. P. and da Costa, Nuno M. C. and Melo, César and Abbasi, Ali and Fonseca, Jaime C. and Cardoso, Paulo and Borges, João",
volume="23",
number="12",
pages="e5610-e5610",
abstract="In the context of Shared Autonomous Vehicles, the need to monitor the environment inside the car will be crucial. This article focuses on the application of deep learning algorithms to present a fusion monitoring solution which was three different algorithms: a violent action detection system, which recognizes violent behaviors between passengers, a violent object detection system, and a lost items detection system. Public datasets were used for object detection algorithms (COCO and TAO) to train state-of-the-art algorithms such as YOLOv5. For violent action detection, the MoLa InCar dataset was used to train on state-of-the-art algorithms such as I3D, R(2+1)D, SlowFast, TSN, and TSM. Finally, an embedded automotive solution was used to demonstrate that both methods are running in real-time.<p /> <p>Language: en</p>",
language="en",
issn="1424-8220",
doi="10.3390/s23125610",
url="http://dx.doi.org/10.3390/s23125610"
}