Driver State Detection from In-Car Camera Images
Identifikátory výsledku
Kód výsledku v 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>
Výsledek na webu
<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>
Alternativní jazyky
Jazyk výsledku
angličtina
Název v původním jazyce
Driver State Detection from In-Car Camera Images
Popis výsledku v původním jazyce
A non-neglectable number of car accidents are caused by driver'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'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.
Název v anglickém jazyce
Driver State Detection from In-Car Camera Images
Popis výsledku anglicky
A non-neglectable number of car accidents are caused by driver'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'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.
Klasifikace
Druh
D - Stať ve sborníku
CEP obor
—
OECD FORD obor
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Návaznosti výsledku
Projekt
—
Návaznosti
S - Specificky vyzkum na vysokych skolach
Ostatní
Rok uplatnění
2022
Kód důvěrnosti údajů
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Údaje specifické pro druh výsledku
Název statě ve sborníku
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
Počet stran výsledku
13
Strana od-do
307-319
Název nakladatele
Springer
Místo vydání
Cham
Místo konání akce
San Diego
Datum konání akce
3. 10. 2022
Typ akce podle státní příslušnosti
WRD - Celosvětová akce
Kód UT WoS článku
—