DriveRNN: Predicting Drivers' Attention with Deep Recurrent Networks
| dc.contributor.author | Lasheras-Hernandez, Blanca | en_US |
| dc.contributor.author | Masia, Belen | en_US |
| dc.contributor.author | Martin, Daniel | en_US |
| dc.contributor.editor | Posada, Jorge | en_US |
| dc.contributor.editor | Serrano, Ana | en_US |
| dc.date.accessioned | 2022-06-22T10:03:49Z | |
| dc.date.available | 2022-06-22T10:03:49Z | |
| dc.date.issued | 2022 | |
| dc.description.abstract | Lately, the automotive industry has experienced a significant development led by the ambitious objective of creating an autonomous vehicle. This entails understanding driving behaviors in different environments, which usually requires gathering and analyzing large amounts of behavioral data from many drivers. However, this is usually a complex and time-consuming task, and data-driven techniques have proven to be a faster, yet robust alternative to modeling drivers' behavior. In this work, we propose a deep learning approach to address this challenging problem. We resort to a novel convolutional recurrent architecture to learn spatio-temporal features of driving behaviors based on RGB sequences of the environment in front of the vehicle. Our model is able to predict drivers' attention in different scenarios while outperforming competing works by a large margin. | en_US |
| dc.description.sectionheaders | Image Understanding, Computer Vision, and Video analytics | |
| dc.description.seriesinformation | Spanish Computer Graphics Conference (CEIG) | |
| dc.identifier.doi | 10.2312/ceig.20221149 | |
| dc.identifier.isbn | 978-3-03868-186-1 | |
| dc.identifier.pages | 65-74 | |
| dc.identifier.pages | 10 pages | |
| dc.identifier.uri | https://doi.org/10.2312/ceig.20221149 | |
| dc.identifier.uri | https://diglib.eg.org:443/handle/10.2312/ceig20221149 | |
| dc.publisher | The Eurographics Association | en_US |
| dc.rights | Attribution 4.0 International License | |
| dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | |
| dc.subject | CCS Concepts: Computing methodologies --> Interest point and salient region detections | |
| dc.subject | Computing methodologies | |
| dc.subject | Interest point and salient region detections | |
| dc.title | DriveRNN: Predicting Drivers' Attention with Deep Recurrent Networks | en_US |
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