Vision-based positioning for internet-of-vehicles

Kuan Wen Chen, Chun Hsin Wang, Xiao Wei, Qiao Liang, Chu Song Chen, Ming Hsuan Yang, Yi Ping Hung

Research output: Contribution to journalArticle

13 Citations (Scopus)

Abstract

This paper presents an algorithm for ego-positioning by using a low-cost monocular camera for systems based on the Internet-of-Vehicles. To reduce the computational and memory requirements, as well as the communication load, we tackle the model compression task as a weighted $k$-cover problem for better preserving the critical structures. For real-world vision-based positioning applications, we consider the issue of large scene changes and introduce a model update algorithm to address this problem. A large positioning data set containing data collected for more than a month, 106 sessions, and 14 275 images is constructed. Extensive experimental results show that submeter accuracy can be achieved by the proposed ego-positioning algorithm, which outperforms existing vision-based approaches.

Original languageEnglish
Article number7506097
Pages (from-to)364-376
Number of pages13
JournalIEEE Transactions on Intelligent Transportation Systems
Volume18
Issue number2
DOIs
Publication statusPublished - 2017 Feb

All Science Journal Classification (ASJC) codes

  • Automotive Engineering
  • Mechanical Engineering
  • Computer Science Applications

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  • Cite this

    Chen, K. W., Wang, C. H., Wei, X., Liang, Q., Chen, C. S., Yang, M. H., & Hung, Y. P. (2017). Vision-based positioning for internet-of-vehicles. IEEE Transactions on Intelligent Transportation Systems, 18(2), 364-376. [7506097]. https://doi.org/10.1109/TITS.2016.2570811