115,182 research outputs found
Towards trajectory anonymization: a generalization-based approach
Trajectory datasets are becoming popular due to the massive usage of GPS and locationbased services. In this paper, we address privacy issues regarding the identification of individuals in static trajectory datasets. We first adopt the notion of k-anonymity to trajectories and propose a novel generalization-based approach for anonymization of trajectories. We further show that releasing
anonymized trajectories may still have some privacy leaks. Therefore we propose a randomization based reconstruction algorithm for releasing anonymized trajectory data and also present how the underlying techniques can be adapted to other anonymity standards. The experimental results on real and synthetic trajectory datasets show the effectiveness of the proposed techniques
Human Motion Trajectory Prediction: A Survey
With growing numbers of intelligent autonomous systems in human environments,
the ability of such systems to perceive, understand and anticipate human
behavior becomes increasingly important. Specifically, predicting future
positions of dynamic agents and planning considering such predictions are key
tasks for self-driving vehicles, service robots and advanced surveillance
systems. This paper provides a survey of human motion trajectory prediction. We
review, analyze and structure a large selection of work from different
communities and propose a taxonomy that categorizes existing methods based on
the motion modeling approach and level of contextual information used. We
provide an overview of the existing datasets and performance metrics. We
discuss limitations of the state of the art and outline directions for further
research.Comment: Submitted to the International Journal of Robotics Research (IJRR),
37 page
PRESS: A Novel Framework of Trajectory Compression in Road Networks
Location data becomes more and more important. In this paper, we focus on the
trajectory data, and propose a new framework, namely PRESS (Paralleled
Road-Network-Based Trajectory Compression), to effectively compress trajectory
data under road network constraints. Different from existing work, PRESS
proposes a novel representation for trajectories to separate the spatial
representation of a trajectory from the temporal representation, and proposes a
Hybrid Spatial Compression (HSC) algorithm and error Bounded Temporal
Compression (BTC) algorithm to compress the spatial and temporal information of
trajectories respectively. PRESS also supports common spatial-temporal queries
without fully decompressing the data. Through an extensive experimental study
on real trajectory dataset, PRESS significantly outperforms existing approaches
in terms of saving storage cost of trajectory data with bounded errors.Comment: 27 pages, 17 figure
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