Smart Road Traffic Monitoring: Unveiling the Synergy of IoT and AI for Enhanced Urban Mobility

Anomaly detection has become an important area of research because of its relevance across a wide range of fields, including surveillance, transportation, healthcare, and public safety. With the rapid expansion of urban environments, the need for effective monitoring systems has increased significantly. Modern cities depend heavily on surveillance infrastructure, particularly CCTV cameras, to observe traffic conditions on roads, highways, and public intersections. While these systems generate a continuous stream of visual data, relying on human operators to monitor them is both impractical and inefficient. Continuous observation can lead to fatigue, reduced attention, and delayed responses, especially when dealing with large-scale surveillance networks. These limitations highlight the necessity for automated systems capable of identifying unusual events accurately and in real time. In this context, the present study focuses on the detection of road accidents using deep learning techniques applied to surveillance video data. Road accidents remain a major global issue, contributing to loss of life, physical injuries, traffic disruption, and economic costs. A critical factor in reducing the impact of such incidents is the speed at which they are detected and reported. Delays in identifying accidents often result in slower emergency response times, which can worsen outcomes. Therefore, there is a clear need for intelligent systems that can recognize accident scenarios as they occur and promptly alert the relevant authorities.

Anomaly Detection, Road Accident Detection, Deep Learning, Computer Vision

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