Efficient Vehicle Counting Based on Time-Spatial Images by Neural Networks

Yu Yun Tseng, Tzu Chien Hsu, Yu Fu Wu, Jen Jee Chen, Yu Chee Tseng

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

Abstract

A highly efficient vehicle counting approach based on timespatial images with deep learning is proposed in this paper. Most vehicle counting solutions are based on frame-by frame object detection and tracking to calculate the number of cars that cross a counting line. However, these approaches incur a great deal of redundancy because they track vehicles in a large area though it matters only when vehicles cross the counting line. In this work, we use time-spatial images to focus only on the information happening along the counting lines, instead of whole images, to reduce redundancy. Due to the nature of time-spatial images, vehicle counting can be achieved by object detection in such images without frame-by-frame tracking. We propose Foreground Favorable Model to conquer occlusion, congestion, and lighting change problems and Cross-Image Object Linking to conquer the distortion problem of nearly static vehicles. We also present an automatic time-spatial image dataset generation flow and the first time-spatial image dataset, called DRIVE-TSI, for vehicle counting tasks. Our vehicle counting accuracy beats state-of-the-art solutions in accuracy and is proved to be much more efficient because it only focuses on a small number of pixels. Our model achieves a 97.95% counting accuracy at 2.91 ms per frame in day time urban scenarios.

Original languageEnglish
Title of host publicationProceedings - 2021 IEEE 18th International Conference on Mobile Ad Hoc and Smart Systems, MASS 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages383-391
Number of pages9
ISBN (Electronic)9781665449359
DOIs
StatePublished - 2021
Event18th IEEE International Conference on Mobile Ad Hoc and Smart Systems, MASS 2021 - Virtual, Online, United States
Duration: 4 Oct 20217 Oct 2021

Publication series

NameProceedings - 2021 IEEE 18th International Conference on Mobile Ad Hoc and Smart Systems, MASS 2021

Conference

Conference18th IEEE International Conference on Mobile Ad Hoc and Smart Systems, MASS 2021
Country/TerritoryUnited States
CityVirtual, Online
Period4/10/217/10/21

Keywords

  • Intelligent transportation system
  • Neural networks
  • Time-spatial image
  • Vehicle counting

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