scholarly journals PSO-Particle Filter-Based Biometric Measurement for Human Tracking

2012 ◽  
Vol 16 (4) ◽  
pp. 533-539
Author(s):  
Zhenyuan Xu ◽  
◽  
Junzo Watada
Keyword(s):  
Mathematical Model ◽  
Particle Filter ◽  
Great Accuracy ◽  
Detection Methods ◽  
Human Tracking ◽  
Surveillance Systems ◽  
Height Measurement ◽  
Swarm Optimization ◽  
Tracking Method ◽  
Biometric Features

Today, security and surveillance systems are required not only to track the motions of humans but also, in some situations, to recognize and measure biometric features such as width and length. Few methods have been proposed for biometric height measurement in human tracking. Some studies have shown that an infrared ray technique can survey the height of a human, but the equipment required is complicated. The objective of this paper is to build a mathematical model to measure the biometrics of human tracking. This tracking method can show humans’ and objects’ size in a picture so that, if we put this picture in a frame of axes, we can calculate the height and other biometric lengths. To obtain the most accurate results for biometric length surveillance, we need a tracking method that is more exact than conventional tracking results. Combining tracking and detection methods using a particle swarm optimization-particle filter shows results with great accuracy in human tracking.

Fault diagnosis based on new particle swarm optimization particle filter

2013 ◽  
Vol 32 (2) ◽  
pp. 432-435
Author(s):  
Zhi-min CHEN ◽  
Yu-ming BO ◽  
Pan-long WU ◽  
Meng-chu TIAN ◽  
Shao-xin LI ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Fault Diagnosis ◽  
Particle Filter ◽  
Particle Swarm ◽  
Swarm Optimization

Tracking method of space IR small target based on improved particle filter

2010 ◽  
Vol 24 (11) ◽  
pp. 1007-1011
Author(s):  
Xuezhi Xiang ◽  
Yu Peng ◽  
Zhiying Han ◽  
Zhihong Xi
Keyword(s):  
Particle Filter ◽  
Small Target ◽  
Tracking Method

scholarly journals INS/gravity gradient aided navigation based on gravitation field particle filter

Open Physics ◽  
2019 ◽  
Vol 17 (1) ◽  
pp. 709-718
Author(s):  
Fanming Liu ◽  
Fangming Li ◽  
Xin Jing
Keyword(s):  
Particle Filter ◽  
Inertial Navigation System ◽  
Attractive Force ◽  
Gravity Gradient ◽  
Experimental Conditions ◽  
Gravitation Field ◽  
Disk Model ◽  
Swarm Optimization ◽  
Repulsion Force ◽  
Position Estimate

Abstract Swarm intelligence method is an effective way to improve the particle degradation and sample depletion of the traditional particle filter. This paper proposes a particle filer based on the gravitation field algorithm (GF-PF), and the gravitation field algorithm is introduced into the resampling process to improve particle degradation and sample depletion. The gravitation field algorithm simulates the solar nebular disk model, and introduces the virtual central attractive force and virtual rotation repulsion force between particles. The particles are moves rapidly to the high-likelihood region under action of the virtual central attractive force. The virtual rotation repulsion force makes the particles keep a certain distance from each other. These operations improve estimation performance, avoid overlapping of particles and maintain the diversity of particles. The proposed method is applied into INS/gravity gradient aided navigation, by combining the sea experimental data of an inertial navigation system. Compared with the particle swarm optimization particle filter(PSO-PF) and artificial physics optimized particle filter (APO-PF), the GF-PF has higher position estimate accuracy and faster convergence speed with the same experimental conditions.

Sign in / Sign up

Export Citation Format

Share Document