TY - JOUR
T1 - Particle filter with gaussian weighting for human tracking
AU - Siradjuddin, Indah Agustien
AU - Widyanto, Muhammad Rahmat
AU - Basaruddin, T.
PY - 2012/1/1
Y1 - 2012/1/1
N2 - 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.
AB - 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.
KW - Bayesian
KW - Particle filter
KW - Prediction
KW - Update
UR - http://www.scopus.com/inward/record.url?scp=84887325426&partnerID=8YFLogxK
U2 - 10.12928/telkomnika.v10i4.869
DO - 10.12928/telkomnika.v10i4.869
M3 - Article
AN - SCOPUS:84887325426
SN - 1693-6930
VL - 10
SP - 801
EP - 806
JO - Telkomnika
JF - Telkomnika
IS - 4
ER -