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.