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Abstract

A novel method for the eight most common driver’s distraction actions recognition is presented in this paper. To this end, a semi-cascade network (SCN) with very lightweight architecture is designed. The approach recognizes the morphology of the human face and hands to make judgments about the driver’s actions rather than just judging facial information. In order to subdivide similar actions, a SCN structure which effectively reduces the network’s scale is employed. A joint training approach is proposed for training the network and achieving 95.61% accuracy. In addition, to verify the validity of the method, a dataset containing 100,000 samples is created. Finally, a warning strategy is provided for our system and 93.9% warning rate for the driver’s distraction behavior is achieved.

Keywords

Advanced driving assistant system convolutional neural network cascade driver monitor system driver’s distraction

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References

Mardi

Ashtiani

SNM

Mikaili

. EEG-based drowsiness detection for safe driving using chaotic features and statistical tests. J Med Sig Sensors 2011; 1: 130–137.

Kim

Jeong

Jung

, et al. Highly reliable driving workload analysis using driver electroencephalogram (EEG) activities during driving. Int J Automot Technol 2013; 14: 965–970.

Sahayadhas

Sundaraj

Murugappan

. Detecting driver drowsiness based on sensors: a review. Sensors 2012; 12: 16937–16953.

Wang

Jeong

Kim

, et al. Drowsy behavior detection based on driving information. Int J Automot Technol 2016; 17: 165–173.

D’Orazio

Leo

Distante

. Eye detection in face images for a driver vigilance system. In: Proceedings of the IEEE intelligent vehicles symposium, Parma, 14–17 June 2004. New York: IEEE.

Liu

Wechsler

. Gabor feature based classification using the enhanced fisher linear discriminant model for face recognition. IEEE Trans Image Process 2002; 11: 467–476.

Rong-Ben

Ke-You

Shu-Ming

, et al. A monitoring method of driver fatigue behavior based on machine vision. In: Proceedings of the IEEE IV2003 intelligent vehicles symposium, Columbus, OH, 9–11 June 2003. New York: IEEE.

Fan

Yin

Sun

. Nonintrusive driver fatigue detection. In: Proceedings of the 2008 IEEE International conference on networking, sensing and control, Sanya, China, 6–8 April 2008. New York: IEEE.

Kim

Lee

Park

, et al. Segmentation method of eye region based on fuzzy logic system for classifying open and closed eyes. Opt Eng 2015; 54: 033103.

10.

Ibrahim

Soraghan

Petropoulakis

. Eye-state analysis using an interdependence and adaptive scale mean shift (IASMS) algorithm. Biomed Sig Process Control 2014; 11: 53–62.

11.

Song

Tan

Liu

, et al. Eyes closeness detection from still images with multi-scale histograms of principal oriented gradients. Pattern Recognit 2014; 47: 2825–2838.

12.

Lee

Jung

, et al. Real-time gaze estimator based on driver’s head orientation for forward collision warning system. IEEE Trans Intell Transport Syst 2011; 12: 254–267.

13.

Murphychutorian

Doshi

Trivedi

. Head pose estimation for driver assistance systems: a robust algorithm and experimental evaluation. In: Proceedings of the 2007 IEEE intelligent transportation systems conference, Seattle, WA, 30 September–3 October 2013. New York: IEEE.

14.

Zhiqiang

Peng

Jingjing

. The study of driver distraction characteristic detection technology. In: Proceedings of the 2010 international forum on information technology and applications, Kunming, China, 16–18 July 2010. New York: IEEE.

15.

Smith

Member

Shah

, et al. Determining driver visual attention with one camera. New York: IEEE Press, 2003.

16.

Lecun

Bottou

Bengio

, et al. Gradient-based learning applied to document recognition. Proc IEEE 1998; 86: 2278–2324.

17.

Howard

Zhu

Chen

, et al. MobileNets: efficient convolutional neural networks for mobile vision applications, 2017, https://arxiv.org/abs/1704.04861

Semi-cascade network for driver’s distraction recognition

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