Detection and tracking of multiple vehicles using semantic information

The alarming global statistics concerning traffic fatalities have prompted society and regulatory bodies to act to enhance road safety. The diverse array of cameras situated along roadways, each with its own set of advantages and limitations, creates a rich landscape of options while also making the development of detection and tracking algorithms more intricate and challenging. To achieve dynamic and reliable tracking, semantic and contextual information from the images is indispensable for forming tracking lines. This paper aims to develop a system for analyzing moving vehicles, encompassing vehicle detection and tracking. We employ the YoloV7 algorithm for initial object detection. In the tracking phase, we leverage semantic information from the vehicles via a series of feature extractors. Finally, the convex hull algorithm allows us to focus on areas of interest while tracking moving vehicles. The results demonstrate that the proposed solution performs comparably to state-of-the-art approaches, which predominantly rely on deep learning techniques.