Revealing the physics of movement: Comparing the similarity of movement characteristics of different types of moving objects

Somayeh Dodge, Robert Weibel, Ehsan Forootan

Research output: Contribution to journalArticlepeer-review

125 Scopus citations

Abstract

We propose a segmentation and feature extraction method for trajectories of moving objects. The methodology consists of three stages: trajectory data preparation; global descriptors computation; and local feature extraction. The key element is an algorithm that decomposes the profiles generated for different movement parameters (velocity, acceleration, etc.) using variations in sinuosity and deviation from the median line. Hence, the methodology enables the extraction of local movement features in addition to global ones that are essential for modeling and analyzing moving objects in applications such as trajectory classification, simulation and extraction of movement patterns. As a case study, we show how the method can be employed in classifying trajectory data generated by unknown moving objects and assigning them to known types of moving objects, whose movement characteristics have been previously learned. We have conducted a series of experiments that provide evidence about the similarities and differences that exist among different types of moving objects. The experiments show that the methodology can be successfully applied in automatic transport mode detection. It is also shown that eye-movement data cannot be successfully used as a proxy of full-body movement of humans, or vehicles.

Original languageEnglish (US)
Pages (from-to)419-434
Number of pages16
JournalComputers, Environment and Urban Systems
Volume33
Issue number6
DOIs
StatePublished - Nov 2009

Keywords

  • Movement behavior
  • Movement parameters
  • Moving object
  • Moving point data mining
  • Trajectory classification
  • Trajectory decomposition

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