Matching Motion Trajectories Using Scale-Space

    Authors

    K. Rangarajan; W. Allen;M. Shah

    Comments

    Authors: contact us about adding a copy of your work at STARS@ucf.edu

    Abbreviated Journal Title

    Pattern Recognit.

    Keywords

    Motion Analysis; Motion Representation; Recognition; Scale-Space; Trajectory Matching; Computer Science, Artificial Intelligence; Engineering, Electrical &; Electronic

    Abstract

    The goal is to design a recognition system which can distinguish between two objects with the same shape but different motion, or between two objects with the same motion but a different shape. The input to the system is a set of two-dimensional (2D) trajectories from an object tracked through a sequence of n frames. The structure and three-dimensional (3D) trajectories of each object in the domain are stored in the model. The problem is to match the information in the model with the input set of 2D trajectories and determine if they represent the same object. The simplest way to perform these steps is to match the input 2D trajectories with the 2D projections of the 3D model trajectories. First, a simple algorithm is presented which matches two single trajectories using only motion information. The 2D motion trajectories are converted into two one-dimensional (1D) signals based on their speed and direction components. The signals are then represented by scale-space images, both to simplify matching and because the scale-space representations are translation and rotation invariant. The matching algorithm is extended to include spatial information and a second algorithm is proposed which matches multiple trajectories by combining motion and spatial match scores. Both algorithms are tested with real and synthetic data.

    Journal Title

    Pattern Recognition

    Volume

    26

    Issue/Number

    4

    Publication Date

    1-1-1993

    Document Type

    Article

    Language

    English

    First Page

    595

    Last Page

    610

    WOS Identifier

    WOS:A1993LD88100012

    ISSN

    0031-3203

    Share

    COinS