short-paper

Real-Time Traffic Classification through Deep Learning

Authors:

Richard SinnottAuthors Info & Claims

BDCAT '21: Proceedings of the 2021 IEEE/ACM 8th International Conference on Big Data Computing, Applications and Technologies

Pages 128 - 133

https://doi.org/10.1145/3492324.3494165

Published: 13 January 2022 Publication History

Abstract

The increasing urbanization of the global population has drawn many researchers’ attention to the field of Intelligent Transportation Systems. Numerous hardware and software technologies have been developed to aid in monitoring and managing the flow of traffic on road networks. As digital cameras become increasingly cheaper and able to produce higher quality images, automated video-based traffic management systems can provide a low cost alternative to conventional (expensive) traffic monitoring systems. In this work we evaluate diverse state-of-the-art deep-learning-based vehicle recognition frameworks on datasets containing surveillance footage of heterogeneous and representative traffic data from Melbourne’s road network. We find that the YOLOv5 family of models offers the optimal balance between detection accuracy, model size, and real-time detection capability for resource-constrained traffic monitoring devices.

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Cited By

Chaudhary STaneja ASingh ARoy PSikdar SMaity MBhattacharya ABagchi SZhang Y(2024)TileClipperProceedings of the 2024 USENIX Conference on Usenix Annual Technical Conference10.5555/3691992.3692051(967-984)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.5555/3691992.3692051
Tang TJayaputera GSinnott R(2024)A Performance Comparison of Convolutional Neural Networks and Transformer-Based Models for Classification of the Spread of Bushfires2024 IEEE 20th International Conference on e-Science (e-Science)10.1109/e-Science62913.2024.10678733(1-9)Online publication date: 16-Sep-2024
https://doi.org/10.1109/e-Science62913.2024.10678733
Kutlimuratov AKhamzaev JKuchkorov TAnwar MChoi A(2023)Applying Enhanced Real-Time Monitoring and Counting Method for Effective Traffic Management in TashkentSensors10.3390/s2311500723:11(5007)Online publication date: 23-May-2023
https://doi.org/10.3390/s23115007

Index Terms

Real-Time Traffic Classification through Deep Learning
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
  2. Machine learning
    1. Learning paradigms
      1. Supervised learning
        Supervised learning by classification
    2. Machine learning approaches
      1. Classification and regression trees
      2. Neural networks

Index terms have been assigned to the content through auto-classification.

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cover image ACM Conferences

BDCAT '21: Proceedings of the 2021 IEEE/ACM 8th International Conference on Big Data Computing, Applications and Technologies

December 2021

133 pages

ISBN:9781450391641

DOI:10.1145/3492324

Copyright © 2021 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 13 January 2022

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BDCAT '21

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SIGARCH

BDCAT '21: 2021 IEEE/ACM 8th International Conference on Big Data Computing, Applications and Technologies

December 6 - 9, 2021

Leicester, United Kingdom

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Overall Acceptance Rate 27 of 93 submissions, 29%

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Cited By

Chaudhary STaneja ASingh ARoy PSikdar SMaity MBhattacharya ABagchi SZhang Y(2024)TileClipperProceedings of the 2024 USENIX Conference on Usenix Annual Technical Conference10.5555/3691992.3692051(967-984)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.5555/3691992.3692051
Tang TJayaputera GSinnott R(2024)A Performance Comparison of Convolutional Neural Networks and Transformer-Based Models for Classification of the Spread of Bushfires2024 IEEE 20th International Conference on e-Science (e-Science)10.1109/e-Science62913.2024.10678733(1-9)Online publication date: 16-Sep-2024
https://doi.org/10.1109/e-Science62913.2024.10678733
Kutlimuratov AKhamzaev JKuchkorov TAnwar MChoi A(2023)Applying Enhanced Real-Time Monitoring and Counting Method for Effective Traffic Management in TashkentSensors10.3390/s2311500723:11(5007)Online publication date: 23-May-2023
https://doi.org/10.3390/s23115007

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