Particle Filter with Constraints for Articulated Upper Body Tracking

2010 ◽  
Vol 439-440 ◽  
pp. 971-976
Author(s):  
Qiong Liu ◽  
Guang Zheng Peng
Keyword(s):  
Particle Filter ◽  
Likelihood Function ◽  
Depth Map ◽  
The State ◽  
Upper Body ◽  
Body Model ◽  
Body Tracking ◽  
Human Pose ◽  
Human Body Tracking ◽  
Number Of Particles

For sophisticated background, a human body tracking algorithm using particle filter based on a 3D articulated body model is introduced. First, a high-fidelity biomechanical upper body model, which is accurate for representing varies complicated human poses and simple to be developed, has been built. Then sequences of images are obtained by using a stereo camera. After calibration, verification and background subtraction, depth map, foreground silhouette, arms skeleton are chosen to construct the likelihood function. The state vectors describing the human pose are computed by fitting the articulated body model to observed person using particle filter. In order to reduce the computational complexity and the number of particles, constraints are employed to restrict the state parameters. Experimental results show that the proposed algorithm can track human upper body with different poses, different person under different illumination conditions fast and accurately.

REAL AND SIMULATED UPPER BODY TRACKING WITH ANNEALING PARTICLE FILTER AND BELIEF PROPAGATION FOR HUMAN–ROBOT INTERACTION

2011 ◽  
Vol 08 (01) ◽  
pp. 127-146 ◽  
Author(s):  
ILARIA RENNA ◽  
RYAD CHELLALI ◽  
CATHERINE ACHARD
Keyword(s):  
Particle Filter ◽  
Belief Propagation ◽  
Human Robot Interaction ◽  
High Dimensionality ◽  
Upper Body ◽  
Computational Time ◽  
Combined Approach ◽  
Challenging Problem ◽  
Robot Interaction ◽  
Body Tracking

This article presents an algorithm for 3D upper body tracking. This algorithm is a combination of two well-known methods: annealing particle filter and belief propagation. It is worth to underline that the 3D body tracking presents a challenging problem because of the high dimensionality of state space and so because of the huge computational time. In this work, we show that with our algorithm, it is possible to tackle this problem. Experiments both on real and synthetic human gesture sequences demonstrate that this combined approach leads to reliable results, as it reduces computational time without loosing robustness.

scholarly journals A Stochastic Covariance Shrinkage Approach to Particle Rejuvenation in the Ensemble Transform Particle Filter

2021 ◽  
Author(s):  
Andrey A. Popov ◽  
Amit N. Subrahmanya ◽  
Adrian Sandu
Keyword(s):  
Particle Filter ◽  
State Space ◽  
Importance Sampling ◽  
Numerical Experiments ◽  
High Probability ◽  
Prior Information ◽  
The State ◽  
Transport Particle ◽  
Ensemble Transform ◽  
Number Of Particles

Abstract. Rejuvenation in particle filters is necessary to prevent the collapse of the weights when the number of particles is insufficient to sample the high probability regions of the state space. Rejuvenation is often implemented in a heuristic manner by the addition of stochastic samples that widen the support of the ensemble. This work aims at improving canonical rejuvenation methodology by the introduction of additional prior information obtained from climatological samples; the dynamical particles used for importance sampling are augmented with samples obtained from stochastic covariance shrinkage. The ensemble transport particle filter, and its second order variant, are extended with the proposed rejuvenation approach. Numerical experiments show that modified filters significantly improve the analyses for low dynamical ensemble sizes.

Particle Filters

2015 ◽  
Author(s):  
Edward P. Herbst ◽  
Frank Schorfheide
Keyword(s):  
Particle Filter ◽  
Bayesian Estimation ◽  
Particle Filtering ◽  
Particle Filters ◽  
Likelihood Function ◽  
Nonlinear Models ◽  
Dsge Models ◽  
Filtering Algorithm ◽  
Bootstrap Filter ◽  
Number Of Particles

This chapter explains how the key difficulty that arises when the Bayesian estimation of DSGE models is extended from linear to nonlinear models is the evaluation of the likelihood function, and focuses on the use of particle filters to accomplish this task. The basic bootstrap particle filtering algorithm is remarkably straightforward, but may perform quite poorly in practice. Thus, much of the literature about particle filters focuses on refinements of the bootstrap filter that increases the efficiency of the algorithm. The accuracy of the particle filter can be improved by choosing other proposal distributions. While the tailoring (or adaption) of the proposal distributions tends to require additional computations, the number of particles can often be reduced drastically, which leads to an improvement in efficiency.

A mutual information‐based likelihood function for particle filter flood extent assimilation

2021 ◽  
Author(s):  
Antara Dasgupta ◽  
Renaud Hostache ◽  
RAAJ Ramasankaran ◽  
Guy J.‐P Schumann ◽  
Stefania Grimaldi ◽  
...  
Keyword(s):  
Mutual Information ◽  
Particle Filter ◽  
Likelihood Function
Sign in / Sign up

Export Citation Format

Share Document