DocumentCode :
3133288
Title :
A probabilistic exclusion principle for tracking multiple objects
Author :
MacCormick, John ; Blake, Andrew
Author_Institution :
Oxford Univ., UK
Volume :
1
fYear :
1999
fDate :
1999
Firstpage :
572
Abstract :
Tracking multiple targets whose models are indistinguishable is a challenging problem. Simply instantiating several independent I-body trackers is not an adequate solution, because the independent trackers can coalesce onto the best-fitting target. This paper presents an observation density for tracking which solves this problem by exhibiting a probabilistic exclusion principle. Exclusion arises naturally from a systematic derivation of the observation density, without relying on heuristics. Another important contribution of the paper is the presentation of partitioned sampling, a new sampling method for multiple object tracking. Partitioned sampling avoids the high computational load associated with fully coupled trackers, while retaining the desirable properties of coupling
Keywords :
image sampling; object recognition; probability; tracking; best-fitting target; independent I-body trackers; multiple objects; observation density; partitioned sampling; probabilistic exclusion principle; sampling method; tracking; Electrical capacitance tomography; Extraterrestrial measurements; Filters; Image segmentation; Layout; Sampling methods; Solids; Target tracking;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computer Vision, 1999. The Proceedings of the Seventh IEEE International Conference on
Conference_Location :
Kerkyra
Print_ISBN :
0-7695-0164-8
Type :
conf
DOI :
10.1109/ICCV.1999.791275
Filename :
791275
Link To Document :
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