Exercise B: Similarity

Task 1: Similarity measures

We will now calculate similarties between trajectories using a new dataset pedestrian.csv (available on moodle). Download an import this dataset as a data.frame or tibble. It it a set of six different but similar trajectories from pedestrians walking on a path.

For this task, explore the trajectories first and get an idea on how the pedestrians moved.

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Task 2: Calculate similarity

Install the package SimilarityMeasures (install.packages("SimilarityMeasures")). Familiarize yourself with this package by skimming through the function descriptions help(package = "SimilarityMeasures"). Now compare trajectory 1 to trajectories 2-6 using different similarity measures from the package. Your options are. DTW, EditDist, Frechet and LCSS.

Before visualizing your results think about the following: Which two trajectories to you percieve to be most similar, which are most dissimilar? Now visualize the results from the computed similarity measures. Which measure reflects your own intuition the closest?

Note:

  • All functions in the package need matrices as input, with one trajectory per matrix.
  • LCSStakes very long to compute. The accuracy of the algorithm (pointSpacing = ,pointDistance = and errorMarg =) can be varied to provide faster calculations. Please see Vlachos, Gunopoulos, and Kollios (2002) for more information.

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