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Proceedings Paper

A machine learning approach for detecting cell phone usage
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Paper Abstract

Cell phone usage while driving is common, but widely considered dangerous due to distraction to the driver. Because of the high number of accidents related to cell phone usage while driving, several states have enacted regulations that prohibit driver cell phone usage while driving. However, to enforce the regulation, current practice requires dispatching law enforcement officers at road side to visually examine incoming cars or having human operators manually examine image/video records to identify violators. Both of these practices are expensive, difficult, and ultimately ineffective. Therefore, there is a need for a semi-automatic or automatic solution to detect driver cell phone usage. In this paper, we propose a machine-learning-based method for detecting driver cell phone usage using a camera system directed at the vehicle’s front windshield. The developed method consists of two stages: first, the frontal windshield region localization using the deformable part model (DPM), next, we utilize Fisher vectors (FV) representation to classify the driver’s side of the windshield into cell phone usage violation and non-violation classes. The proposed method achieved about 95% accuracy with a data set of more than 100 images with drivers in a variety of challenging poses with or without cell phones.

Paper Details

Date Published: 4 March 2015
PDF: 8 pages
Proc. SPIE 9407, Video Surveillance and Transportation Imaging Applications 2015, 94070A (4 March 2015); doi: 10.1117/12.2083126
Show Author Affiliations
Beilei Xu, PARC, A Xerox Co. (United States)
Robert P. Loce, PARC, A Xerox Co. (United States)


Published in SPIE Proceedings Vol. 9407:
Video Surveillance and Transportation Imaging Applications 2015
Robert P. Loce; Eli Saber, Editor(s)

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