Lane detection in surveillance videos using vector-based hierarchy clustering and density verification

Shan Yun Teng, Kun Ta Chuang, Chun Rong Huang, Cheng Chun Li

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Automatic lane detection is known to facilitate the real-time traffic planning and identify traffic congestion. In this paper, we develop a visual surveillance trajectory clustering (VSTC) framework for automatic lane detection. Given a surveillance video, trajectories of vehicles are extracted at first. These trajectories contain behavior of vehicles on different lanes and are clustered by VSTC to retrieve candidate lanes. Finally, a density verification is applied to identify the correct lanes from candidate lanes. As shown in the experiments, our framework can identify the lanes by using trajectories without prior knowledge.

Original languageEnglish
Title of host publicationProceedings of the 14th IAPR International Conference on Machine Vision Applications, MVA 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages345-348
Number of pages4
ISBN (Electronic)9784901122153
DOIs
Publication statusPublished - 2015 Jul 8
Event14th IAPR International Conference on Machine Vision Applications, MVA 2015 - Tokyo, Japan
Duration: 2015 May 182015 May 22

Publication series

NameProceedings of the 14th IAPR International Conference on Machine Vision Applications, MVA 2015

Other

Other14th IAPR International Conference on Machine Vision Applications, MVA 2015
CountryJapan
CityTokyo
Period15-05-1815-05-22

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Computer Vision and Pattern Recognition

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    Teng, S. Y., Chuang, K. T., Huang, C. R., & Li, C. C. (2015). Lane detection in surveillance videos using vector-based hierarchy clustering and density verification. In Proceedings of the 14th IAPR International Conference on Machine Vision Applications, MVA 2015 (pp. 345-348). [7153201] (Proceedings of the 14th IAPR International Conference on Machine Vision Applications, MVA 2015). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/MVA.2015.7153201