Detecting Drivers Smartphone: A Learned Features Approach using Aggregated Scalogram Images

In this paper, we propose an image representation approach for detecting driver mobile phone from the accelerometer signals produced by a set of smartphones in a vehicle. Rather than following the classic paradigm of classifying the signal as driver or non-driver, we propose an original paradigm whe...

Full description

Saved in:
Bibliographic Details
Published in2019 IEEE/ACS 16th International Conference on Computer Systems and Applications (AICCSA) pp. 1 - 5
Main Authors Madine, Mohammad, Battah, Ammar, Khan, Aaminah, Werghi, Naoufel
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.11.2019
Subjects
Accelerometers
Automobiles
CNN classification
Feature extraction
Hardware
image fusion
Image representation
Indexes
learned features
Smart phones
smartphone sensing
Online AccessGet full text

Cover

More Information
Summary:In this paper, we propose an image representation approach for detecting driver mobile phone from the accelerometer signals produced by a set of smartphones in a vehicle. Rather than following the classic paradigm of classifying the signal as driver or non-driver, we propose an original paradigm whereby we aggregate the signals together and train a classifier to detect the driver signal in that aggregation. We do so by stacking-up the Scalograms images of the smartphone signals and training a CNN classifier to identify the driver's Scalograms instance in the Scalograms stack image. To the best our knowledge, this is the first time such an image-fusion and classification scheme is proposed for detecting driver's smartphone. Experiments performed with an in-house dataset confirms the potential and the merit of our approach.
ISSN:2161-5330
DOI:10.1109/AICCSA47632.2019.9035262