Trajectory segmentation using greedy best rst change point detection based on trajectory similarity
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2021-06-25
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en
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Abstract
In day to day life nding structure in information is easy for humans, but
this is not so easy for smart machine learning methods. They require structured
data in order to be smart and become smarter, the more data, the
better. There is a immense amount of data in existence, but most of this
data is unstructured. Especially trajectory data, that consists of a series of
x and y coordinates, is di cult to structure because of the large variety of
information it can represent.
Many methods exist that can nd structure in trajectory data by splitting
it into many small segments, creating a trajectory segmentation. Many of
these methods lack the ability to t to patterns of di erent length and thus
struggle to nd segments of di erent lengths. Many methods also use complex
approaches that are di cult to reason about and thus to generalize to
other data, where a simple rule-based strategy could yield similar results.
Most notably trajectory similarity methods are simple methods to compare
trajectories. They are an ideal candidate to be used in the context of trajectory
segmentation, but are scarcely being used.
We identify a new solution that uses a trajectory similarity-based cost function
and multiple temporal time scales to identify the points of largest pattern
change, by tting windows to patterns in the data. This has the simple
intuition that, if any two patterns are very di erent, they are likely to be different
processes and should thus be separate segments. In a comprehensive
test using numerous di erent parameter con guration on a dataset consisting
of 5 trajectories of mice, we compare our method to two of the best
change point detection methods: Binary segmentation and Pelt. The results
indicate that our method achieves the best performance when the window
size can be optimized for the data. When our method is not optimized for
the data, Pelt generally has the better performance.
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Faculteit der Sociale Wetenschappen