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📄 Abstract
Abstract: Traffic congestion is becoming a challenge in the rapidly growing urban
cities, resulting in increasing delays and inefficiencies within urban
transportation systems. To address this issue a comprehensive methodology is
designed to optimize traffic flow and minimize delays. The framework is
structured with three primary components: (a) vehicle detection, (b) traffic
prediction, and (c) traffic signal optimization. This paper presents the first
component, vehicle detection. The methodology involves analyzing multiple
sequential frames from a camera feed to compute the background, i.e. the
underlying roadway, by averaging pixel values over time. The computed
background is then utilized to extract the foreground, where the Density-Based
Spatial Clustering of Applications with Noise (DBSCAN) algorithm is applied to
detect vehicles. With its computational efficiency and minimal infrastructure
modification requirements, the proposed methodology offers a practical and
scalable solution for real-world deployment.
Submitted
October 28, 2025
Key Contributions
This paper introduces a novel methodology for real-time vehicle detection in traffic analysis. It utilizes background averaging from sequential camera frames and DBSCAN for efficient vehicle identification, offering a practical solution for optimizing traffic flow and reducing delays in urban environments.
Business Value
Enables smarter traffic management systems, leading to reduced travel times, lower fuel consumption, and improved urban logistics. This can translate to significant cost savings for transportation companies and better quality of life for city dwellers.