Document Type

Article

Publication Date

2020

Keywords

convolutional neural networks; computer vision; cognitive load; distractive behavior

Abstract

The risk of pedestrian accidents has increased due to the distracted walking increase. The research in the autonomous vehicles industry aims to minimize this risk by enhancing the route planning to produce safer routes. Detecting distracted pedestrians plays a significant role in identifying safer routes and hence decreases pedestrian accident risk. Thus, this research aims to investigate how to use the convolutional neural networks for building an algorithm that significantly improves the accuracy of detecting distracted pedestrians based on gathered cues. Particularly, this research involves the analysis of pedestrian’ images to identify distracted pedestrians who are not paying attention when crossing the road. This work tested three different architectures of convolutional neural networks. These architectures are Basic, Deep, and AlexNet. The performance of the three architectures was evaluated based on two datasets. The first is a new training dataset called SCIT and created by this work based on recorded videos of volunteers from Sheridan College Institute of Technology. The second is a public dataset called PETA, which was made up of images with various resolutions. The ConvNet model with the Deep architecture outperformed the Basic and AlexNet architectures in detecting distracted pedestrian.

Faculty

Faculty of Applied Science & Technology (FAST)

Journal

International Journal of Advanced Computer Science and Applications(IJACSA)

Volume

11

Issue

2

First Page

630

Last Page

638

Version

Publisher's version

Terms of Use

Terms of Use for Works posted in SOURCE.

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Original Publication Citation

Grishchenko, I and Mahmoud, E (2020) An investigation of a convolution neural network architecture for detecting distracted pedestrians. International Journal of Advanced Computer Science and Applications(IJACSA),11(2), 630-638. http://dx.doi.org/10.14569/IJACSA.2020.0110279

Share

COinS