Vision-Based Vehicle Detection in Traffic Surveillance: Reducing Accidents Through Smart Traffic Monitoring

This book presents a comprehensive study of vision-based vehicle detection and traffic analysis for urban road systems. It addresses the longstanding challenges faced by city planners in controlling traffic and ensuring public safety. The text explains how traffic monitoring involves counting vehicles, classifying them, and allocating appropriate signal timings to optimize flow. Image processing techniques, including detection and counting algorithms, are discussed in detail. Vehicles are detected using Haar-Cascade Classifiers, and their counts are used to determine lane-specific signaling times. The book also explores advanced methods in vehicle classification, traffic density estimation, and real-time monitoring. Combining theory with practical examples, it provides a clear understanding of computer vision applications in intelligent traffic management. It serves as an essential guide for students, researchers, and professionals in computer science, engineering, and urban planning, offering both technical depth and real-world applicability in traffic surveillance and control systems.