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Deep Dissimilarity Measure for Trajectory Analysis

  • Reza Arfa
  • Rubiyah YusofEmail author
  • Parvaneh Shabanzadeh
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 946)

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.

Keywords

Trajectory analysis Dissimilarity measure Deep learning LSTM 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • Reza Arfa
    • 1
  • Rubiyah Yusof
    • 1
    Email author
  • Parvaneh Shabanzadeh
    • 1
  1. 1.Centre for Artificial Intelligence and Robotics, Malaysia-Japan International Institute of Technology (MJIIT)Universiti Teknologi MalaysiaKuala LumpurMalaysia

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