Comprehensive Analysis to Detect Optimal Vehicle Position for Roadside Traffic Surveillance Using Lightweight Contour-Based CNN

In the realm of transport development, the fusion of modern technology and vehicle surveillance in roadside areas becomes indispensable. Traditional surveillance demands continuous monitoring through closed-circuit television cameras. It results in a huge amount of data, which requires high computation. This study delves into the challenges of real-time processing of vehicle surveillance within smart cities with quality data. In addition to a specific focus on monitoring the roadside traffic region despite technological advancements, including target variability, lighting conditions, and occlusion, the manuscript introduces a lightweight contour-based convolutional neural network to address these challenges. The proposed work aims to gain the maximum features from the vehicle via detecting the optimal position and incorporating a Region-Proposal-Network, Region-of-Interest-Align and pooling, Non-Maximum-Suppression, Structural-Similarity-Index, and Peak-Signal-to-Noise-Ratio. The proposed work extracts hierarchical information from a custom video dataset and demonstrates superior performance with an accuracy rate of 97.36% and a minimum loss of 0.0816 in an elapsed time of 1s 159ms. Furthermore, it achieves a validation loss of 0.1506, and a validation accuracy of 96.46%. Additionally, manuscripts illustrate different datasets and models through a systematic literature review. Moreover, the manuscript also illustrates the Smart-City framework and Integrated Traffic Management System architecture.

• Record URL:
  http://dx.doi.org/10.18280/ijtdi.080119
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/20588305
• Supplemental Notes:
  • © 2024 The authors. This article is published by IIETA and is licensed under the CC BY 4.0 license.
• Authors:
  • Sharma, Nand Kishore
  • 0000-0001-8260-1739
  • Rahamatkar, Surendra
  • 0000-0002-1211-0560
  • Rathore, Abhishek Singh
  • 0000-0002-5513-2639
• Publication Date: 2024-3

Language

• English

Media Info

• Media Type: Web
• Features: Figures; Photos; References; Tables;
• Pagination: pp 197-213
• Serial:
  • International Journal of Transport Development and Integration
  • Volume: 8
  • Issue Number: 1
  • Publisher: International Information and Engineering Technology Association
  • ISSN: 2058-8305
  • EISSN: 2058-8313
  • Serial URL: https://iieta.org/Journals/IJTDI
• Publication flags:

  Open Access (libre)

Subject/Index Terms

• TRT Terms: Automatic vehicle monitoring; Image analysis; Neural networks; Real time data processing; Roadside; Traffic surveillance
• Subject Areas: Data and Information Technology; Highways; Operations and Traffic Management;

Filing Info

• Accession Number: 01918336
• Record Type: Publication
• Files: TRIS
• Created Date: May 13 2024 4:33PM