logo
CanyamAI summaries for academic research
language
MobileMobile
  • Phone FeatureWeChat Mini Program
EnglishEnglish
noticeNotice
BlogBlog
delete
arrow
Return

Kernel Particle Filter for visual tracking

delete2005-03-01
delete199
PRE
AI
C
Cheng Chang
R
Rashid Ansari
DOI：10.1109/LSP.2004.842254delete
deleteOriginal
deleteOriginal request for help
deleteShare
deleteSave
Abstract

Abstract

En 中文
A new particle filter-the Kernel Particle Filter (KPF)-is proposed for visual tracking in image sequences. The KPF invokes kernels to form a continuous estimate of the posterior density function. Particles are allocated based on the gradient information estimated from the kernel density estimate of the posterior. Results from simulations and experiments with real video data show the improved performance of the proposed algorithm when compared with that of the standard particle filter. The superior performance is evident in scenarios of small system noise or weak dynamic models where the standard particle filter usually fails.
Keywords:
bootstrap filter
kernel density estimation
mean shift
particle filter
target tracking

Journal

IEEE Signal Processing Magazine cover
IEEE Signal Processing Magazine
IF:
9.6
Papers:
1.1W
Citations:
1.7W

Organization

No organization information available