Semi-cascade network for driver's distraction recognition. (August 2019)

• Record Type: Journal Article
• Authors: Hu, Jun; Liu, Wei; Kang, Jiawen; Yang, Wenxing; Zhao, Hong
• Other Names: Yang Diange guest-editor.
• Is Part Of: Proceedings of the Institution of Mechanical Engineers. Volume 233:Number 9(2019:Sep.)
• Page Start: 2323
• Page End: 2332
• Publication Date: 2019-08
• Subjects: Advanced driving assistant system -- convolutional neural network -- cascade -- driver monitor system -- driver's distraction; Mechanical engineering -- Congresses; Transportation engineering -- Congresses; 629.2
• DOI: 10.1177/0954407019857408
• Languages: English
• ISSNs: 0954-4070

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.