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

The use of mobiles phones when driving has been a major factor when it comes to road traffic i ncidents 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 w ith o ur c ustom d ataset using four popular object detection methods: YOLO, SSD, Faster R-CNN, and CenterNet. Out of all the object detectors tested, YOLO yields the highest accuracy levels of up to ∼96% (AP
10
) 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.