Trajectory Abstracting with Group-Based Signal Denoising

Trajectory abstracting is to compendiously summarize the substance of a lot of information delivered by the trajectory data. In this paper, to cope with complex trajectory data, we propose a novel framework for abstracting trajectories from the perspective of signal processing. That is, trajectories are designated as signals, manifesting the copious information that varies with time and space, and denoising is exploited to concisely communicate the trajectory data. Resampling of trajectory data is firstly performed, based on achieving the minimum Jensen-Shannon divergence of the trajectories before and after being re-sampled. The resampled trajectories are matched into groups according to their similarity and, a non-local denoising approach based on wavelet transformation is developed to produce summaries of trajectory groups. Our new framework can not only offer multi-granularity abstractions of trajectory data, but also identify outlier trajectories. Extensive experimental studies have shown that the proposed framework achieves very potential results in trajectory summarization, in terms of both objective evaluation metrics and subjective visual effects. To the best of our knowledge, this is the first to deploy the group-based signal denoising technique in the context of summarizing the trajectory data.