ENHANCING TRAFFIC SURVEILLANCE WITH INTEGRATED AI-BASED SOLUTIONS

Abstract

This research paper presents an innovative approach to enhance traffic surveillance using integrated artificial intelligence (AI) techniques. The focus is on detecting speeding, red light violations, and illegal parking in urban intersections and crowded markets. We propose an integrated model that combines object detection, motion analysis, and anomaly detection, supported by scalable real-time processing hardware. Ethical considerations and privacy implications of AI-based surveillance systems are discussed in the context of their public space application. Experimental results demonstrate the effectiveness of the proposed approach, supported by a literature review of recent advancements.