  E-scooters have become ubiquitous vehicles in major cities around the
world.The numbers of e-scooters keep escalating, increasing their interactions
with other cars on the road. Normal behavior of an e-scooter rider varies
enormously to other vulnerable road users. This situation creates new
challenges for vehicle active safety systems and automated driving
functionalities, which require the detection of e-scooter riders as the first
step. To our best knowledge, there is no existing computer vision model to
detect these e-scooter riders. This paper presents a novel vision-based system
to differentiate between e-scooter riders and regular pedestrians and a
benchmark data set for e-scooter riders in natural scenes. We propose an
efficient pipeline built over two existing state-of-the-art convolutional
neural networks (CNN), You Only Look Once (YOLOv3) and MobileNetV2. We
fine-tune MobileNetV2 over our dataset and train the model to classify
e-scooter riders and pedestrians. We obtain a recall of around 0.75 on our raw
test sample to classify e-scooter riders with the whole pipeline. Moreover, the
classification accuracy of trained MobileNetV2 on top of YOLOv3 is over 91%,
with precision and recall over 0.9.
