Driver State Detection from In-Car Camera Images

被引：2

作者：

Fusek, Radovan ^{[1
]}

Sojka, Eduard ^{[1
]}

Gaura, Jan ^{[1
]}

Halman, Jakub ^{[1
]}

机构：

[1] VSB Tech Univ Ostrava, Dept Comp Sci, Fac Elect Engn & Comp Sci, 17 Listopadu 2172-15, Ostrava 70800, Czech Republic

来源：

ADVANCES IN VISUAL COMPUTING, ISVC 2022, PT II | 2022年 / 13599卷

关键词：

Anomaly detection; Driver assistance systems; Deep learning; Autoencoder; LSTM;

D O I：

10.1007/978-3-031-20716-7_24

中图分类号：

TP31 [计算机软件];

学科分类号：

081202 ; 0835 ;

摘要：

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.

引用

页码：307 / 319

页数：13

共 30 条

[1]

Abadi M, 2016, PROCEEDINGS OF OSDI'16: 12TH USENIX SYMPOSIUM ON OPERATING SYSTEMS DESIGN AND IMPLEMENTATION, P265

[2] LOF: Identifying density-based local outliers [J].

Breunig, MM ;

Kriegel, HP ;

Ng, RT ;

Sander, J .

SIGMOD RECORD, 2000, 29 (02) :93-104

[3]

Cao Z, 2019, Arxiv, DOI [arXiv:1812.08008, 10.48550/arXiv.1812.08008]

[4]

Choi IH, 2016, INT CONF BIG DATA, P143, DOI 10.1109/BIGCOMP.2016.7425813

[5]

Chollet F., 2015, Keras

[6]

Fusek R., 2022, MRL DRIVER DATASET

[7]

Goodfellow I. J., 2014, arXiv, DOI DOI 10.48550/ARXIV.1406.2661

[8]

He KM, 2015, Arxiv, DOI arXiv:1512.03385

[9]

Hochreiter S, 1997, NEURAL COMPUT, V9, P1735, DOI [10.1162/neco.1997.9.8.1735, 10.1162/neco.1997.9.1.1, 10.1007/978-3-642-24797-2]

[10] ImageNet Classification with Deep Convolutional Neural Networks [J].

Krizhevsky, Alex ;

Sutskever, Ilya ;

Hinton, Geoffrey E. .

COMMUNICATIONS OF THE ACM, 2017, 60 (06) :84-90

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