Integral invariants for space motion trajectory matching and recognition

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

30 Scopus Citations
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Author(s)

Detail(s)

Original languageEnglish
Pages (from-to)2418-2432
Journal / PublicationPattern Recognition
Volume48
Issue number8
Online published11 Mar 2015
Publication statusPublished - Aug 2015

Abstract

Abstract Motion trajectories provide a key and informative clue in motion characterization of humans, robots and moving objects. In this paper, we propose some new integral invariants for space motion trajectories, which benefit effective motion trajectory matching and recognition. Integral invariants are defined as the line integrals of a class of kernel functions along a motion trajectory. A robust estimation of the integral invariants is formulated based on the blurred segment of noisy discrete curve. Then a non-linear distance of the integral invariants is defined to measure the similarity for trajectory matching and recognition. Such integral invariants, in addition to being invariant to transformation groups, have some desirable properties such as noise insensitivity, computational locality, and uniqueness of representation. Experimental results on trajectory matching and sign recognition show the effectiveness and robustness of the proposed integral invariants in motion trajectory matching and recognition.

Research Area(s)

  • Integral invariants, Maximal blurred segment, Motion trajectory, Sign recognition, Similarity measure, Trajectory matching

Citation Format(s)

Integral invariants for space motion trajectory matching and recognition. / Shao, Zhanpeng; Li, Youfu.
In: Pattern Recognition, Vol. 48, No. 8, 08.2015, p. 2418-2432.

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review