Implementation of A.I. vehicle detection for traffic analysis using in-situ surveillance infrastructure

Abstract

Traffic flow parameters are required for optimizing traffic operations, design of pavements, and future planning of traffic networks. Unfortunately, due to the unique characteristics and variety of vehicles in the sub-continent i.e., size and design, the accuracy of results for a vision-based system is challenged, since most thorough datasets are based on European and American traffic. This paper proposes a solution by developing a detection model ground-up using a dataset created from the local traffic surveillance footage, and creating a python pipeline for vehicle speed detection and classification. The vehicle classification model is developed using the state-of-the-art YOLO object detector which significantly reduces the computation time required to maintain the efficiency of the proposed solution. Furthermore, a computer-vision script is developed to track the movement of vehicles in the footage and record the speeds in a spreadsheet. The technique used eliminates the video calibration, including distance and angle, required for detecting accurate speeds. Finally, the real-time traffic data is analyzed to derive the fundamental traffic flow parameters and discuss the relation between flow and density. To ascertain the validity of this survey technique, the results are compared to the following renowned traffic flow models: The Modified Greenberg model, Eddie’s model, and The Two-regime model. The results are found to closely follow the models in all three cases.