  With over 50 million car sales annually and over 1.3 million deaths every
year due to motor accidents we have chosen this space. India accounts for 11
per cent of global death in road accidents. Drivers are held responsible for
78% of accidents. Road safety problems in developing countries is a major
concern and human behavior is ascribed as one of the main causes and
accelerators of road safety problems. Driver distraction has been identified as
the main reason for accidents. Distractions can be caused due to reasons such
as mobile usage, drinking, operating instruments, facial makeup, social
interaction. For the scope of this project, we will focus on building a highly
efficient ML model to classify different driver distractions at runtime using
computer vision. We would also analyze the overall speed and scalability of the
model in order to be able to set it up on an edge device. We use CNN, VGG-16,
RestNet50 and ensemble of CNN to predict the classes.
