Encoding bird's trajectory using Recurrent Neural Networks

Ilya S. Ardakani, Koichi Hashimoto

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

2 Citations (Scopus)

Abstract

Recurrent Neural Networks (RNNs) are currently state of art tools for processing and classifying data sequences. This work aims to exploit these capabilities in Long-Short Term Memory (LSTM) networks which are a powerful variant of RNNs for encoding the birds' trajectory data into state vectors. These vectors should encapsulate the contextual information about the immediate trajectory coordinates. Therefore, they can generate new trajectory points based on their state and the latest output. However, probabilistic behavior of birds, effects of environment and noisy nature of measurements pose challenges for training and testing of the LSTM network models. This study solely focuses on the effects of spatial context and their significance in subsequent outputs to achieve compact representation of the traversed trajectory. Therefore, trajectory coordinates of birds were used as input to LSTM networks to learn spatial path features encoded in hidden vectors of the network. In the end, t-SNE method is used to visualize the state vectors in lower dimensional space embeddings and It was observed that these vectors contained contextual information about the traversed path.

Original languageEnglish
Title of host publication2017 IEEE International Conference on Mechatronics and Automation, ICMA 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1644-1649
Number of pages6
ISBN (Electronic)9781509067572
DOIs
Publication statusPublished - 2017 Aug 23
Event14th IEEE International Conference on Mechatronics and Automation, ICMA 2017 - Takamatsu, Japan
Duration: 2017 Aug 62017 Aug 9

Publication series

Name2017 IEEE International Conference on Mechatronics and Automation, ICMA 2017

Other

Other14th IEEE International Conference on Mechatronics and Automation, ICMA 2017
CountryJapan
CityTakamatsu
Period17/8/617/8/9

Keywords

  • Bio-navigation
  • LSTM Auto-Encoder
  • Machine learning
  • Recurrent Neural Networks

ASJC Scopus subject areas

  • Control and Optimization
  • Instrumentation
  • Artificial Intelligence
  • Industrial and Manufacturing Engineering
  • Mechanical Engineering

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  • Cite this

    Ardakani, I. S., & Hashimoto, K. (2017). Encoding bird's trajectory using Recurrent Neural Networks. In 2017 IEEE International Conference on Mechatronics and Automation, ICMA 2017 (pp. 1644-1649). [8016063] (2017 IEEE International Conference on Mechatronics and Automation, ICMA 2017). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICMA.2017.8016063