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Data-Driven Trajectory Smoothing

Abstract : Motivated by the increasing availability of large collections of noisy GPS traces, we present a new data-driven framework for smoothing trajectory data. The framework, which can be viewed of as a generalization of the classical moving average technique, naturally leads to efficient algorithms for various smoothing objectives. We analyze an algorithm based on this framework and provide connections to previous smoothing techniques. We implement a variation of the algorithm to smooth an entire collection of trajectories and show that it perform well on both synthetic data and massive collections of GPS traces.
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https://hal.inria.fr/inria-00629932
Contributor : Frédéric Chazal <>
Submitted on : Friday, October 7, 2011 - 2:43:18 AM
Last modification on : Friday, February 23, 2018 - 2:20:08 PM
Long-term archiving on: : Sunday, January 8, 2012 - 2:21:11 AM

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  • HAL Id : inria-00629932, version 1

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Frédéric Chazal, Daniel Chen, Leonidas J. Guibas, Xiaoye Jiang, Christian Sommer. Data-Driven Trajectory Smoothing. [Research Report] RR-7754, INRIA. 2011. ⟨inria-00629932⟩

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