Enhancing Road Safety Through Computer Vision: A Comprehensive Approach Using Associative Rule Mining

Ensuring road safety is of utmost importance in contemporary urban settings, requiring creative strategies to prevent accidents and efficiently manage traffic. In this paper, we present a solution that utilizes computer vision techniques, with a specific emphasis on using associative rule mining. Our approach combines face tracking and vehicle detection using associative rule mining (ARM) to gain a comprehensive picture of traffic dynamics and improve road safety. The face tracking component enables the detection of distracted drivers or pedestrians, providing valuable information about potential safety risks. Our research adds to the expanding knowledge in the field of intelligent transportation systems by introducing a new framework that tackles the complex difficulties of road safety. This framework combines face tracking, vehicle identification, and associative rule mining to achieve its objectives. The proposed method shows potential for developing road networks that are both safer and more efficient in the age of smart cities.