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3D trajectory matching by pose normalization

Published: 04 November 2005 Publication History

Abstract

Recent technological advances have made it possible to collect large amounts of 3D trajectory data. Such data play an essential role in numerous applications and are becoming increasingly important in mobile computing. One of the fundamental challenges in many of these application areas is the assessment of similarity between trajectories. As objects moving in a 3D space may often exhibit a similar motion pattern but may differ in location, orientation, and scale, the similarity assessment method employed must be invariant to these seven degrees of freedom. Previous work has addressed this problem primarily through local measures, such as curvature and torsion and has mostly concentrated on 2D trajectory data. This paper introduces a novel non iterative 3D trajectory matching framework that is translation, rotation, and scale invariant. We achieve this through the introduction of a pose normalization process that is based on physical principles, which incorporates both spatial and temporal aspects of trajectory data. We also introduce a new shape signature that utilizes the invariance that is achieved through pose normalization. The proposed scheme was tested both on simulated data and on real world data and has shown to offer improved robustness compared to local measures.

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cover image ACM Conferences
GIS '05: Proceedings of the 13th annual ACM international workshop on Geographic information systems
November 2005
306 pages
ISBN:1595931465
DOI:10.1145/1097064
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 04 November 2005

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Author Tags

  1. 3D trajectory
  2. matching
  3. pose normalization
  4. spatiotemporal analysis

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  • (2022)Efficient Characterization Method for Big Automotive Datasets Used for Perception System Development and VerificationIEEE Access10.1109/ACCESS.2022.314519210(12629-12643)Online publication date: 2022
  • (2020)Simultaneous Heterogeneous Sensor Localization, Joint Tracking, and Upper Extremity Modeling for Stroke RehabilitationIEEE Systems Journal10.1109/JSYST.2020.296384214:3(3570-3581)Online publication date: Sep-2020
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  • (2015)3D motion matching algorithm using signature feature descriptorMultimedia Tools and Applications10.1007/s11042-014-2103-274:3(1125-1136)Online publication date: 1-Feb-2015
  • (2014)Gesture Learning by Imitation Architecture for a Social RobotRobotics10.4018/978-1-4666-4607-0.ch014(274-294)Online publication date: 2014
  • (2014)Chinese sign language recognition with 3D hand motion trajectories and depth imagesProceeding of the 11th World Congress on Intelligent Control and Automation10.1109/WCICA.2014.7052933(1457-1461)Online publication date: Jun-2014
  • (2014)Intelligent Trajectory Classification for Improved Movement PredictionIEEE Transactions on Systems, Man, and Cybernetics: Systems10.1109/TSMC.2014.231674244:10(1301-1314)Online publication date: Oct-2014
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  • (2014)Decomposition and dictionary learning for 3D trajectoriesSignal Processing10.1016/j.sigpro.2013.12.00498(423-437)Online publication date: 1-May-2014
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