Enhancing Traffic Surveillance and Urban Mobility with Vision-Based Vehicle Analysis
S Kanagamalliga, P Kovalan, K Kiran, S Rajalingam · 2023
Covering a spectrum of approaches from traditional methods to cutting-edge deep learning models, this comprehensive examination delves into the latest advancements in vehicle detection, recognition, and tracking through vision-based methodologies, offering a thorough analysis of their strengths, limitations, and realworld applications. Beginning with the evolution of vehicle detection methods, it explores traditional techniques like edge-based methods and motion segmentation approaches such as frame differencing, background subtraction, and optical flow, showcasing their efficacy in identifying moving vehicles and their historical significance in early traffic surveillance systems. Emphasizing the transformative impact of Convolutional Neural Networks (CNNs), particularly in challenging scenarios marked by occlusion and varying lighting conditions, the paper details how CNNs have revolutionized vehicle detection accuracy. Beyond detection, it examines vehicle recognition and classification techniques such as colour recognition, license plate recognition, logo recognition, and vehicle type classification, highlighting their practical applications in traffic analysis, security, and urban planning. The exploration extends to vehicle tracking strategies, encompassing model -based, shape -based, and feature based approaches, with a comparative analysis of tracking algorithms based on factors like accuracy, computational efficiency, and adaptability. This comprehensive research offers valuable insights for researchers, practitioners, and policymakers aiming to advance traffic surveillance systems and shape the future of urban mobility through vision-based vehicle analysis.