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Deep Dissimilarity Measure for Trajectory Analysis

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Methods and Applications for Modeling and Simulation of Complex Systems (AsiaSim 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 946))

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Abstract

Quantifying dissimilarities between two trajectories is a challenging yet fundamental task in many trajectory analysis systems. Existing methods are computationally expensive to calculate. We proposed a dissimilarity measure estimate for trajectory data by using deep learning methodology. One advantage of the proposed method is that it can get executed on GPU, which can significantly reduce the execution time for processing large number of data. The proposed network is trained using synthetic data. A simulator to generate synthetic trajectories is proposed. We used a publicly available dataset to evaluate the proposed method for the task of trajectory clustering. Our experiments show the performance of our proposed method is comparable with other well-known dissimilarity measures while it is substantially faster to compute.

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Authors and Affiliations

  1. Centre for Artificial Intelligence and Robotics, Malaysia-Japan International Institute of Technology (MJIIT), Universiti Teknologi Malaysia, 54100, Kuala Lumpur, Malaysia

    Reza Arfa, Rubiyah Yusof & Parvaneh Shabanzadeh

Authors
  1. Reza Arfa
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  2. Rubiyah Yusof
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  3. Parvaneh Shabanzadeh
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Corresponding author

Correspondence to Rubiyah Yusof .

Editor information

Editors and Affiliations

  1. Ritsumeikan University, Kusatsu, Shiga, Japan

    Liang Li

  2. Ritsumeikan University, Kusatsu, Shiga, Japan

    Kyoko Hasegawa

  3. Ritsumeikan University, Kusatsu, Shiga, Japan

    Satoshi Tanaka

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© 2018 Springer Nature Singapore Pte Ltd.

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Arfa, R., Yusof, R., Shabanzadeh, P. (2018). Deep Dissimilarity Measure for Trajectory Analysis. In: Li, L., Hasegawa, K., Tanaka, S. (eds) Methods and Applications for Modeling and Simulation of Complex Systems. AsiaSim 2018. Communications in Computer and Information Science, vol 946. Springer, Singapore. https://doi.org/10.1007/978-981-13-2853-4_11

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  • DOI: https://doi.org/10.1007/978-981-13-2853-4_11

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-2852-7

  • Online ISBN: 978-981-13-2853-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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