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

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Steven Carrell , Amir Atapour–Abarghouei ·

YOU? · · 2021 · Open Access · · DOI: https://doi.org/10.1109/bigdata52589.2021.9671378

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

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Computer Science

Object Detection

Computer Vision

Artificial Intelligence

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Computer science Phone Object detection Object (grammar) Identification (biology) Detector Video tracking Frame (networking) Computer vision Artificial intelligence Task (project management) Process (computing) Tracking (education) Frame rate Real-time computing Pattern recognition (psychology) Engineering Telecommunications Psychology Linguistics Philosophy Systems engineering Operating system Pedagogy Botany Biology

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Type: article
Language: en
Landing Page: https://doi.org/10.1109/bigdata52589.2021.9671378
OA Status: gold
Cited By: 8
References: 43
Related Works: 10
OpenAlex ID: https://openalex.org/W3196427668

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DOI: https://doi.org/10.1109/bigdata52589.2021.9671378

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

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Type: article

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Language: en

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Publication year: 2021

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Publication date: 2021-12-15

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Authors: Steven Carrell, Amir Atapour–Abarghouei

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Landing page: https://doi.org/10.1109/bigdata52589.2021.9671378

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Concepts: Computer science, Phone, Object detection, Object (grammar), Identification (biology), Detector, Video tracking, Frame (networking), Computer vision, Artificial intelligence, Task (project management), Process (computing), Tracking (education), Frame rate, Real-time computing, Pattern recognition (psychology), Engineering, Telecommunications, Psychology, Linguistics, Philosophy, Systems engineering, Operating system, Pedagogy, Botany, Biology

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Cited by: 8

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Citations by year (recent): 2025: 3, 2023: 2, 2022: 3

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References (count): 43

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