Context-based semantical vector representations for animal trajectories

Ilya Ardakani, Koichi Hashimoto, Ken Yoda

Research output: Contribution to journalArticlepeer-review


Behaviour could be expressed as a set of specific movement patterns in time. An animal's movement or trajectory could characterise its behaviours and provide information about its internal states. Recent advances in GPS-based sensor technologies led to drastic increase in volume of the data collected from animals' movements which enables researchers to analyse and model their behaviours using data-driven methods. However, having compact, discriminative, semantical and independent numerical representations of trajectories as features, is essential for employing the most of available off-the-shelf machine learning and deep learning techniques. Inspired by language processing, the approach presented in this study utilizes Skip-gram model to create contextual vector embeddings or representations of key-points in animal trajectories to be used as input features. Here, a key-point is defined as a location which represents a trajectory segment. It is assumed that these key-points encapsulate contextual information which is attributed to a certain behaviour or specific group of animals with similar behavioural features. So, the vector embeddings could be interpreted as contextual semantical representations of trajectory key-points independent of their spatial coordinates. With these representations, it would be possible to predict likelihood of preceding or subsequent key-points given a context or an internal state, or vice versa. To test this hypothesis, an experiment was conducted on birds' trajectories logged from a seabird species, Streaked Shearwater (Calonectris leucomelas). In this experiment, vector representations of the key-points in birds' trajectories were constructed and optimized using candidate sampling. The experimental results showcased the utility of these vector embeddings in both exploration of Streaked Shearwater trajectory data and improvement of gender-based trajectory classification. In summary, the proposed method provided a novel approach for numerical representation of animal trajectories and, it was illustrated to be semantically more explanatory for analysis as well as being more informative as features for modelling of animal movement data.

Original languageEnglish
Pages (from-to)118-133
Number of pages16
JournalAdvanced Robotics
Issue number3-4
Publication statusPublished - 2019 Feb 16


  • Bio-navigation
  • animal movement and behaviour analytics
  • animal trajectory mining
  • trajectory vector embeddings

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Human-Computer Interaction
  • Hardware and Architecture
  • Computer Science Applications


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