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An Algorithm for Mining Moving Flock Patterns from Pedestrian Trajectories

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Web Technologies and Applications (APWeb 2016)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9865))

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Abstract

Statistics from the research show that the majority of pedestrians actually do not walk alone, but in groups. Detecting pedestrians moving together through public spaces can provide valuable information to many location-aware applications. In this paper, we use the term “degree of freedom” to reflect the characteristics of pedestrian freedom of movement, and propose a definition of freedom moving flock pattern and a corresponding extraction algorithm. Furthermore, we evaluate the proposed algorithm by applying it to two real pedestrian trajectory datasets. The results show that the algorithm is capable of extracting the freedom moving flock patterns that are common in real-world scenarios.

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Acknowledgements

This research was supported by the Major Project of High Resolution Earth Observation System (No. 11-Y20A40-9002-15/17)

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Authors and Affiliations

  1. School of Computer Science, South China Normal University, Guangzhou, China

    Yang Cao, Jia Zhu & Fang Gao

Authors
  1. Yang Cao
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  2. Jia Zhu
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  3. Fang Gao
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Corresponding author

Correspondence to Yang Cao .

Editor information

Editors and Affiliations

  1. University of Tsukuba , Tsukuba, Japan

    Atsuyuki Morishima

  2. East China Normal University , Shanghai, China

    Rong Zhang

  3. University of New South Wales , Sydney, New South Wales, Australia

    Wenjie Zhang

  4. The University of New South Wales, School of Computer Science and Engineering, Sydney, Australia

    Lijun Chang

  5. Advanced Digital Sciences Center, Illinois at Singapore Pte Ltd, Advanced Digital Sciences Center, Fusionopolies Way, Singapore

    Tom Z. J Fu

  6. Hyundai Motor Tower, Pivotal Inc 15/F, Hyundai Motor Tower, Chaoyang, China

    Kuien Liu

  7. Illinois at Singapore Pte Ltd, Advanced Digital Sciences Center , Singapore, Singapore

    Xiaoyan Yang

  8. South China Normal University, School of Computer Science, Guangzhou, China

    Jia Zhu

  9. Department of Computer Science, Hong Kong Baptist University Department of Computer Science, Kowloon Tong, Hong Kong

    Zhiwei Zhang

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Cao, Y., Zhu, J., Gao, F. (2016). An Algorithm for Mining Moving Flock Patterns from Pedestrian Trajectories. In: Morishima, A., et al. Web Technologies and Applications. APWeb 2016. Lecture Notes in Computer Science(), vol 9865. Springer, Cham. https://doi.org/10.1007/978-3-319-45835-9_27

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  • DOI: https://doi.org/10.1007/978-3-319-45835-9_27

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-45834-2

  • Online ISBN: 978-3-319-45835-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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