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

Carrell, Steven; Atapour-Abarghouei, Amir

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, December). Identification of Driver Phone Usage Violations via State-of-the-Art Object Detection with Tracking. Presented at 2021 IEEE International Conference on Big Data (IEEE BigData 2021), Orlando, FL, USA

• Presentation Conference Type: Conference Paper (published)
• Conference Name: 2021 IEEE International Conference on Big Data (IEEE BigData 2021)
• 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
• Public URL: https://durham-repository.worktribe.com/output/1138899