Computational-geometric methods for polygonal approximations of a curve
Computer Vision, Graphics, and Image Processing
LeZi-update: an information-theoretic approach to track mobile users in PCS networks
MobiCom '99 Proceedings of the 5th annual ACM/IEEE international conference on Mobile computing and networking
Information Theory, Inference & Learning Algorithms
Information Theory, Inference & Learning Algorithms
Indexing spatio-temporal trajectories with Chebyshev polynomials
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Techniques for Efficient Road-Network-Based Tracking of Moving Objects
IEEE Transactions on Knowledge and Data Engineering
Spatio-temporal data reduction with deterministic error bounds
The VLDB Journal — The International Journal on Very Large Data Bases
Indexing Spatio-Temporal Trajectories with Efficient Polynomial Approximations
IEEE Transactions on Knowledge and Data Engineering
Remote real-time trajectory simplification
PERCOM '09 Proceedings of the 2009 IEEE International Conference on Pervasive Computing and Communications
Sketch-Based Interfaces and Modeling (SBIM): Sketching piecewise clothoid curves
Computers and Graphics
Usability analysis of compression algorithms for position data streams
Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems
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The trace of a moving object is commonly referred to as a trajectory. This paper considers the spatio-temporal information content of a discrete trajectory in relation to a movement prediction model for the object under consideration. The information content is the minimal amount of information necessary to reconstruct the trajectory, given the movement model. We show how the information content of arbitrary trajectories can be determined and use these findings to derive an approximative arithmetic coding scheme for trajectory information, reaching a level of compression that is close to the bound provided by its entropy. We then demonstrate the practical applicability of our ideas by using them to compress real-world vehicular trajectories, showing that this vastly improves upon the results provided by the best state-of-the art compression schemes for spatio-temporal data.