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Effect of Resampling Steepness on Particle Filtering Performance in Visual Tracking

2010
Journal from gdlhub / 2017-08-14 11:52:32
Oleh : Zahidul Islam, Chi-Min Oh, Chil-Woo Lee, IAJIT
Dibuat : 2012-06-23, dengan 1 file

Keyword : Resampling, particle filter, multi-part colour histogram, steepness parameter, object tracking.
Subjek : Effect of Resampling Steepness on Particle Filtering Performance in Visual Tracking
Url : http://www.ccis2k.org/iajit/PDF/vol.10,no.1/4177-4.pdf
Sumber pengambilan dokumen : Internet

This paper presents a proficiently developed resampling Algorithm for particle filtering. In any filtering Algorithm


adopting the perception of particles, especially in visual tracking, resampling is an essential process that determines the


Algorithm’s performance and accuracy in the implementation step. It is usually a linear function of the weight of the particles,


which determines the number of particles copied. If we use many particles to prevent sample impoverishment, however, the


system becomes computationally too expensive. For better real-time performance with high accuracy, we introduce a Steep


Sequential Importance Resampling (S-SIR) Algorithm that can require fewer highly weighted particles by introducing a


nonlinear function into the resampling method. Using our proposed Algorithm, we have obtained very remarkable results for


visual tracking with only a few particles instead of many. Dynamic parameter setting boosts the steepness of resampling and


reduces computational time without degrading performance. Since resampling is not dependent on any particular application,


the S-SIR analysis is appropriate for any type of particle filtering Algorithm that adopts a resampling procedure. We show that


the S-SIR Algorithm can improve the performance of a complex visual tracking Algorithm using only a few particles compared


with a traditional SIR-based particle filter.

Deskripsi Alternatif :

This paper presents a proficiently developed resampling Algorithm for particle filtering. In any filtering Algorithm


adopting the perception of particles, especially in visual tracking, resampling is an essential process that determines the


Algorithm’s performance and accuracy in the implementation step. It is usually a linear function of the weight of the particles,


which determines the number of particles copied. If we use many particles to prevent sample impoverishment, however, the


system becomes computationally too expensive. For better real-time performance with high accuracy, we introduce a Steep


Sequential Importance Resampling (S-SIR) Algorithm that can require fewer highly weighted particles by introducing a


nonlinear function into the resampling method. Using our proposed Algorithm, we have obtained very remarkable results for


visual tracking with only a few particles instead of many. Dynamic parameter setting boosts the steepness of resampling and


reduces computational time without degrading performance. Since resampling is not dependent on any particular application,


the S-SIR analysis is appropriate for any type of particle filtering Algorithm that adopts a resampling procedure. We show that


the S-SIR Algorithm can improve the performance of a complex visual tracking Algorithm using only a few particles compared


with a traditional SIR-based particle filter.

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