Factored particle filtering with dependent and constrained partition dynamics for tracking deformable objects
Yükleniyor...
Tarih
2014-10
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Springer
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
In particle filtering, dimensionality of the state space can be reduced by tracking control (or feature) points as independent objects, which are traditionally named as partitions. Two critical decisions have to be made in implementation of reduced state-space dimensionality. First is how to construct a dynamic (transition) model for partitions that are inherently dependent. Second critical decision is how to filter partition states such that a viable and likely object state is achieved. In this study, we present a correlation-based transition model and a proposal function that incorporate partition dependency in particle filtering in a computationally tractable manner. We test our algorithm on challenging examples of occlusion, clutter and drastic changes in relative speeds of partitions. Our successful results with as low as 10 particles per partition indicate that the proposed algorithm is both robust and efficient.
Açıklama
Anahtar Kelimeler
Particle filtering, Condensation, Factorized likelihoods, Deterministic drift, Proposal function, Multiple objects, Integration
Kaynak
Machine Vision and Applications
WoS Q DeÄŸeri
Q2
Scopus Q DeÄŸeri
Q2
Cilt
25
Sayı
7
Künye
Eskil, M. T. (2014). Factored particle filtering with dependent and constrained partition dynamics for tracking deformable objects. Machine Vision and Applications, 25(7), 1825-1840. doi:10.1007/s00138-014-0634-1
