  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.
