Automatic identification of vehicles from single view

Abstract

Transportation has always been part and parcel of our daily lives. Getting from one place to another and getting there safely in one piece is two different sides of a coin. Be it public transport, private hire or your own vehicle, safety of our well-being is always paramount. With the increasing number of vehicles on the road, your safety might be dependent on the other drivers, no matter how safe you are. Thus, with the use of AI applications to perceives the vehicle’s surrounding environment, identify and keep track of multiple objects (moving or stationary), we are able to reduce the possibilities of tragedies from happening.

An autonomous vehicle detection system will be developed through the process of this project, whereby it incorporates the YOLOv4 CSPDarknet 53 architecture to train on images of objects required for detection. The integration of DeepSORT tracker will assist the system in tracking the objects within the camera’s field of view, allowing the system to know the location of each unique objects detected. Implementation of counter, distance and speed estimation techniques allows the system to detect if other drivers are too near, too fast and pose a danger to the user.