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Proceedings Paper

Multiassignment for tracking a large number of overlapping objects
Author(s): Thiagalingam Kirubarajan; Yaakov Bar-Shalom; Krishna R. Pattipati
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Paper Abstract

In this paper we present a new technique for data association using multiassignment for tracking a large number of closely spaced (and overlapping) objects. The algorithm is illustrated on a biomedical problem, namely the tracking of a group of fibroblast (tissue) cells from an image sequence, which motivated this work. The algorithm presents a novel iterated approach to multiassignment using successive one-to-one assignments of decreasing size with modified costs. The cost functions, which are adjusted depending on the 'depth' of the current assignment level and on the tracking results, are derived. The resulting assignments are used to form, maintain and terminate tracks with a modified version of the probabilistic data association filter, which can handle the contention for a single measurement among multiple tracks in addition to the association of multiple measurements to a single track. Estimation results are given and compared with those of the standard 2-dimensional one-to-one assignment algorithm. It is shown that iterated multiassignment results in superior measurement-to- track association.

Paper Details

Date Published: 29 October 1997
PDF: 12 pages
Proc. SPIE 3163, Signal and Data Processing of Small Targets 1997, (29 October 1997); doi: 10.1117/12.283967
Show Author Affiliations
Thiagalingam Kirubarajan, Univ. of Connecticut (United States)
Yaakov Bar-Shalom, Univ. of Connecticut (United States)
Krishna R. Pattipati, Univ. of Connecticut (United States)


Published in SPIE Proceedings Vol. 3163:
Signal and Data Processing of Small Targets 1997
Oliver E. Drummond, Editor(s)

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