Identification of Driver Phone Usage Violations via State-of-the-Art Object Detection with Tracking

Carrell, Steven; Atapour-Abarghouei, Amir

doi:10.1109/bigdata52589.2021.9671378

Identification of Driver Phone Usage Violations via State-of-the-Art Object Detection with Tracking

Carrell, Steven; Atapour-Abarghouei, Amir

Authors

Steven Carrell

Dr Amir Atapour-Abarghouei amir.atapour-abarghouei@durham.ac.uk
Assistant Professor

Abstract

The use of mobiles phones when driving has been a major factor when it comes to road traffic incidents and the process of capturing such violations can be a laborious task. Advancements in both modern object detection frameworks and high-performance hardware has paved the way for a more automated approach when it comes to video surveillance. In this work, we propose a custom-trained state-of-the-art object detector to work with roadside cameras to capture driver phone usage without the need for human intervention. The proposed approach also addresses the issues caused by windscreen glare and introduces the steps required to remedy this. Twelve pretrained models are fine-tuned with our custom dataset using four popular object detection methods: YOLO, SSD, Faster RCNN, and CenterNet. Out of all the object detectors tested, YOLO yields the highest accuracy levels of up to ∼96% (AP10) and frame rates of up to ∼30 FPS. DeepSORT object tracking algorithm is also integrated into the best-performing model in order to avoid logging duplicate violations.

Citation

Carrell, S., & Atapour-Abarghouei, A. (2021). Identification of Driver Phone Usage Violations via State-of-the-Art Object Detection with Tracking. . https://doi.org/10.1109/bigdata52589.2021.9671378

Conference Name	2021 IEEE International Conference on Big Data (IEEE BigData 2021)
Conference Location	Orlando, FL, USA
Start Date	Dec 15, 2021
End Date	Dec 18, 2021
Acceptance Date	Oct 28, 2021
Publication Date	Dec 15, 2021
Deposit Date	Dec 2, 2021
Publicly Available Date	Dec 3, 2021
Publisher	Institute of Electrical and Electronics Engineers
ISBN	9781665445993
DOI	https://doi.org/10.1109/bigdata52589.2021.9671378

Files

Accepted Conference Proceeding (1.1 Mb)
PDF

Copyright Statement
© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.