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

Steven Carrell; Amir Atapour-Abarghouei

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arXiv · 2109.02119

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

Steven Carrell·Amir Atapour-Abarghouei

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Abstract

The use of mobiles phones when driving have 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 pre-trained models are fine-tuned with our custom dataset using four popular object detection methods: YOLO, SSD, Faster R-CNN, and CenterNet. Out of all the object detectors tested, the 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 to collect records of only the unique violations, and enable the proposed approach to count the number of vehicles. The proposed automated system will collect the output images of the identified violations, timestamps of each violation, and total vehicle count. Data can be accessed via a purpose-built user interface.

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BibTeX RIS

Steven Carrell, Amir Atapour-Abarghouei. 2021-10-08. Identification of Driver Phone Usage Violations via State-of-the-Art Object Detection with Tracking. https://arxiv.org/abs/2109.02119

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