A noniterative greedy algorithm for multiframe point correspondence

    Authors

    K. Shafique;M. Shah

    Comments

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    Abbreviated Journal Title

    IEEE Trans. Pattern Anal. Mach. Intell.

    Keywords

    point correspondence; target tracking; motion; occlusion; point; trajectory; data association; bipartite graph matching; path cover of; directed graph; MOTION CORRESPONDENCE; 3-DIMENSIONAL MOTION; MULTIPLE TARGETS; DATA; ASSOCIATION; TRACKING; TRAJECTORIES; IMAGES; Computer Science, Artificial Intelligence; Engineering, Electrical &; Electronic

    Abstract

    This paper presents a framework for finding point correspondences in monocular image sequences over multiple frames. The general problem of multiframe point correspondence is NP-hard for three or more frames. A polynomial time algorithm for a restriction of this problem is presented and is used as the basis of the proposed greedy algorithm for the general problem. The greedy nature of the proposed algorithm allows it to be used in real-time systems for tracking and surveillance, etc. In addition, the proposed algorithm deals with the problems of occlusion, missed detections, and false positives by using a single noniterative greedy optimization scheme and, hence, reduces the complexity of the overall algorithm as compared to most existing approaches where multiple heuristics are used for the same purpose. While most greedy algorithms for point tracking do not allow for entry and exit of the points from the scene, this is not a limitation for the proposed algorithm. Experiments with real and synthetic data over a wide range of scenarios and system parameters are presented to validate the claims about the performance of the proposed algorithm.

    Journal Title

    Ieee Transactions on Pattern Analysis and Machine Intelligence

    Volume

    27

    Issue/Number

    1

    Publication Date

    1-1-2005

    Document Type

    Article

    Language

    English

    First Page

    51

    Last Page

    65

    WOS Identifier

    WOS:000225028200006

    ISSN

    0162-8828

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