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A REVIEW OF STATISTICAL-DATA ASSOCIATION TECHNIQUES FOR MOTION CORRESPONDENCE

COX, IJ; (1993) A REVIEW OF STATISTICAL-DATA ASSOCIATION TECHNIQUES FOR MOTION CORRESPONDENCE. INT J COMPUT VISION , 10 (1) 53 - 66.

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Abstract

Motion correspondence is a fundamental problem in computer vision and many other disciplines. This article describes statistical data association techniques originally developed in the context of target tracking and surveillance and now beginning to be used in dynamic motion analysis by the computer vision community. The Mahalanobis distance measure is first introduced before discussing the limitations of nearest neighbor algorithms. Then, the track-splitting, joint likelihood, multiple hypothesis algorithms are described, each method solving an increasingly more complicated optimization. Real-time constraints may prohibit the application of these optimal methods. The suboptimal joint probabilistic data association algorithm is therefore described. The advantages, limitations, and relationships between the approaches are discussed.

Type: Article
Title: A REVIEW OF STATISTICAL-DATA ASSOCIATION TECHNIQUES FOR MOTION CORRESPONDENCE
Keywords: MULTIPLE TARGETS, TRACKING
UCL classification: UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: http://discovery.ucl.ac.uk/id/eprint/135616
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