Detection of Distracted Driver using Convolution Neural Network
This addresses road safety issues, particularly in developing countries like India, by focusing on driver distraction as a major cause of accidents, but it appears incremental as it applies existing CNN methods to this domain.
The paper tackles the problem of detecting distracted drivers to reduce road accidents by developing a machine learning model that classifies different driver distractions using computer vision, achieving unspecified performance metrics.
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.