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

Carrell, Steven and Atapour-Abarghouei, Amir (2021) 'Identification of Driver Phone Usage Violations via State-of-the-Art Object Detection with Tracking.', 2021 IEEE International Conference on Big Data (IEEE BigData 2021)


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.

Item Type:Conference item (Paper)
Full text:(AM) Accepted Manuscript
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Publisher 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.
Date accepted:28 October 2021
Date deposited:03 December 2021
Date of first online publication:2021
Date first made open access:03 December 2021

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