Local Complexity Adaptable Trajectory Partitioning via Minimum Message Length

We present a minimum message length (MML) framework for trajectory partitioning by point selection, and use it to automatically select the tolerance parameter ε for Douglas-Peucker partitioning, adapting to local trajectory complexity. By examining a range of ε for synthetic and real trajectories, it is easy to see that the best ε does vary by trajectory, and that the MML encoding makes sensible choices and is robust against Gaussian noise. We use it to explore the identification of micro-activities within a longer trajectory. This MML metric is comparable to the TRACLUS metric – and shares the constraint of abstracting only by omission of points – but is a true lossless encoding. Such encoding has several theoretical advantages – particularly with very small segments (high frame rates) – but actual performance interacts strongly with the search algorithm. Both differ from unconstrained piecewise linear approximations, including other MML formulations.
Keywords MML  Minimum Message Length  Trajectory Partitioning  Compression  Segmentation  Encoding  MDL  Minimum Description Length  Abstraction
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PhilPapers Archive Charles R. Twardy, Local Complexity Adaptable Trajectory Partitioning via Minimum Message Length
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