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- W2390744223 abstract "In multi-target tracking problem,when the number of objects to track is known,standard Bayesian methods like PDA/JPDA can be employed.However,when the number of objects to track is unknown or varies over time,tracking hypothesis with different numbers of objects have to be compared.This can be addressed by viewing the set of object as a random set,in which the number of objects,N,is a stochastic variable.Tracking of random sets is formulated with finite set statistics (FISST).The contribution of this paper is a particle implementation of tracking random sets.Experiments show the particle filter to be able to estimate both the number of tracked objects,as well as the states of the objects,robustly from noisy observations." @default.
- W2390744223 created "2016-06-24" @default.
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- W2390744223 date "2006-01-01" @default.
- W2390744223 modified "2023-09-25" @default.
- W2390744223 title "PHD particle filtering for random sets" @default.
- W2390744223 hasPublicationYear "2006" @default.
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