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DOI: 10.14569/IJACSA.2021.0120911
PDF

Traffic Adaptive Deep Learning based Fine Grained Vehicle Categorization in Cluttered Traffic Videos

Author 1: Shobha B. S
Author 2: Deepu. R

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 12 Issue 9, 2021.

  • Abstract and Keywords
  • How to Cite this Article
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Abstract: Smart traffic management is being proposed for better management of traffic infrastructure and regulate traffic in smart cities. With surge of traffic density in many cities, smart traffic management becomes utmost necessity. Vehicle categorization, traffic density estimation and vehicle tracking are some of the important functionalities in smart traffic management. Vehicles must be categorized based on multiple levels like type, speed, direction of travel and vehicle attributes like color etc. for efficient tracking and traffic density estimation. Vehicle categorization becomes very challenging due to occlusions, cluttered backgrounds and traffic density variations. In this work, a traffic adaptive multi-level vehicle categorization using deep learning is proposed. The solution is designed to solve the problems in vehicle categorization in terms of occlusions, cluttered backgrounds.

Keywords: Vehicle categorization; deep learning; traffic density estimation; clutter

Shobha B. S and Deepu. R, “Traffic Adaptive Deep Learning based Fine Grained Vehicle Categorization in Cluttered Traffic Videos” International Journal of Advanced Computer Science and Applications(IJACSA), 12(9), 2021. http://dx.doi.org/10.14569/IJACSA.2021.0120911

@article{S2021,
title = {Traffic Adaptive Deep Learning based Fine Grained Vehicle Categorization in Cluttered Traffic Videos},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2021.0120911},
url = {http://dx.doi.org/10.14569/IJACSA.2021.0120911},
year = {2021},
publisher = {The Science and Information Organization},
volume = {12},
number = {9},
author = {Shobha B. S and Deepu. R}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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