Abstract

In this paper, we propose a camera-based unified framework for automated traffic analysis in unstructured environments. The proposed framework utilizes computer vision and deep learning-based algorithms on the camera feed of an infrastructure camera observing a traffic scene. Detection and tracking of vehicles infer information regarding their spatial position over time. This information is further utilized to perform crucial tasks such as class-wise and direction-wise vehicle detection and counting, traffic density and volume estimation, and the detection of wrongly parked vehicles as well as wrong-side driving. The experimentation leverages an real-world traffic dataset of an unstructured driving environment to show the efficacy of the proposed system. The insights derived from this framework are vital for understanding complex traffic patterns, enabling informed optimization of traffic management strategies, and ultimately enhancing road safety in challenging, real-world conditions.

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