Accurate Vehicles Detection and Speed Estimation Using Homography Based Background Subtraction and Deep Learning Approaches

In the context of the ever-evolving landscape of information and communication technology, urban populations worldwide are increasingly embracing the notion of smart cities. Smart transportation, a fundamental component of the smart cities, falls under the purview of what is commonly referred to as the Intelligent Transportation System (ITS). This system plays a pivotal role in the management of highway transportation infrastructure. A key facet of this system involves the widespread installation of CCTV cameras on urban thoroughfares. These cameras serve a dual function, diligently monitoring traffic conditions and detecting anomalies such as traffic congestion and violations of prescribed vehicle speed limits. This research study is primarily dedicated to the task of estimating vehicle speeds using two distinct methodologies: background subtraction based approach and deep learning based approach. Both methodologies leverage the concept of homography based inverse perspective projection to achieve precise vehicle detection and speed estimation. The study is underpinned by the utilization of two distinct datasets, one for the purpose of training and the other for estimating vehicle speeds. The initial phase of our investigation focuses on the accurate detection of vehicles. To accomplish this, we trained a YOLOv8 model, yielding impressive outcomes. In the realm of vehicle detection accuracy, the background subtraction based method achieved an accuracy rate of 92.14%, while the deep learning approach demonstrated an even higher level of accuracy, standing at around 98.88%. Then, our research shifts its focus to the tracking of vehicles frame-by-frame and the subsequent calculation of their speeds. This is achieved with the aid of two reference lines in both vehicle detection methodologies. The results obtained from our experiments unequivocally highlight the superior speed estimation capabilities of deep learning approaches when compared to the background subtraction based method.