Particle Filter with Gaussian Weighting for Human Tracking
Abstract: Particle filter for
object tracking could achieve high tracking accuracy. To track the object, this
method generates a number of particles which is the representation of the
candidate target object. The location of target object is determined by
particles and each weight. The disadvantage of conventional particle filter is
the computational time especially on the computation of particle’s weight.
Particle filter with Gaussian weighting is proposed to accomplish the
computational problem. There are two main stages in this method, i.e.
prediction and update. The difference between the conventional particle filter
and particle filter with Gaussian weighting is in the update Stage. In the
conventional particle filter method, the weight is calculated in each particle,
meanwhile in the proposed method, only certain particle’s weight is calculated,
and the remain particle’s weight is calculated using the Gaussian weighting.
Experiment is done using artificial dataset. The average accuracy is 80,862%.
The high accuracy that is achieved by this method could use for the real-time
system tracking
Author: Indah Agustien
Siradjuddin, M. Rahmat Widyanto, T. Basaruddin
Journal Code: jptkomputergg120110
