Traffic Management through Cutting-Edge Vehicle Detection, Recognition, and Tracking Innovations
S Kanagamalliga, P Kovalan, K Kiran, S Rajalingam
Abstract
In this research, we explore the latest advancements in the domains of vehicle detection, recognition, and tracking through vision-based methodologies. Spanning from traditional techniques to cutting-edge deep learning models, the paper provides a thorough analysis of their strengths, limitations, and real-world applications. We examine the evolution of detection methods, including edge-based approaches and motion segmentation techniques like frame differencing, background subtraction, and optical flow, showcasing their efficacy in identifying moving vehicles. Convolutional neural networks (CNNs), in particular, are highlighted for the integration of appearance-based approaches due to their transformative impact on accuracy, particularly in difficult settings. In addition, we explore vehicle recognition and classification methods, highlighting their applications in traffic analysis, security, and urban planning. These methods include color recognition, license plate recognition, logo recognition, and vehicle type classification. In addition to comparing tracking algorithms based on accuracy, computing economy, and adaptability, the evaluation includes methodologies for tracking vehicles that include model-based, shape-based, and feature-based approaches. In conclusion, this research provides a thorough examination of the development of vision-based vehicle analysis, making it a useful tool for academics, professionals, and decision-makers working to improve traffic surveillance systems and plan for the future of urban transportation.