Driver State Detection from In-Car Camera Images

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F22%3A10251061" target="_blank" >RIV/61989100:27240/22:10251061 - isvavai.cz</a>

Result on the web

<a href="https://link.springer.com/chapter/10.1007/978-3-031-20716-7_24" target="_blank" >https://link.springer.com/chapter/10.1007/978-3-031-20716-7_24</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-031-20716-7_24" target="_blank" >10.1007/978-3-031-20716-7_24</a>

Result language

angličtina

Original language name

Driver State Detection from In-Car Camera Images

Original language description

A non-neglectable number of car accidents are caused by driver&apos;s loss of ability to drive the car, which may be caused by serious health problems, e.g. heart attack, stroke, drug or alcohol influence, as well as by drowsiness and other problems. In this paper, a method is presented for detecting the anomaly situations during driving. The method is based on detecting the particular parts of driver&apos;s body in the sequence of images obtained from an in-car camera. A feature vector containing the distances between the body parts and describing the situation in a chosen number of frames is computed and used for detection. For the detection itself, the neural network of the autoencoder type containing the LSTM units is used. The method is compared with some other methods; the results show that the method is useful. Moreover, the video sequences used for training and testing are presented, which may be regarded as an additional contribution. (C) 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

—

Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2022

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Volume 13599

ISBN

978-3-031-20715-0

ISSN

0302-9743

e-ISSN

1611-3349

Number of pages

13

Pages from-to

307-319

Publisher name

Springer

Place of publication

Cham

Event location

San Diego

Event date

Oct 3, 2022

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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