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A Unified Framework for Camera-based Automated Traffic Analysis in Unstructured Environments

by Vivek Kumar Paswan, Ayesha Choudhary
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 187 - Number 57
Year of Publication: 2025
Authors: Vivek Kumar Paswan, Ayesha Choudhary
10.5120/ijca2025925968

Vivek Kumar Paswan, Ayesha Choudhary . A Unified Framework for Camera-based Automated Traffic Analysis in Unstructured Environments. International Journal of Computer Applications. 187, 57 ( Nov 2025), 1-8. DOI=10.5120/ijca2025925968

@article{ 10.5120/ijca2025925968,
author = { Vivek Kumar Paswan, Ayesha Choudhary },
title = { A Unified Framework for Camera-based Automated Traffic Analysis in Unstructured Environments },
journal = { International Journal of Computer Applications },
issue_date = { Nov 2025 },
volume = { 187 },
number = { 57 },
month = { Nov },
year = { 2025 },
issn = { 0975-8887 },
pages = { 1-8 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume187/number57/a-unified-framework-for-camera-based-automated-traffic-analysis-in-unstructured-environments/ },
doi = { 10.5120/ijca2025925968 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2025-11-18T21:11:12.084544+05:30
%A Vivek Kumar Paswan
%A Ayesha Choudhary
%T A Unified Framework for Camera-based Automated Traffic Analysis in Unstructured Environments
%J International Journal of Computer Applications
%@ 0975-8887
%V 187
%N 57
%P 1-8
%D 2025
%I Foundation of Computer Science (FCS), NY, USA
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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Index Terms

Computer Science
Information Sciences

Keywords

Traffic Analysis; Traffic Volume; Wrongly Parked Vehicle; Wrong Direction Driving; Road Safety; Intelligent Transportation Systems

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