Visual tracking using Particle Filter and Monte Carlo Markov Chain
نویسنده
چکیده
Tracking is an important processing step for many single and multi-camera applications such as sports video analysis, traffic monitoring and event detection. In the first part of the paper, we present a framework of visual tracking using first-order Markov state-space model. We subsequently use Sequential importance Sampling method to estimate the posterior density and obtain the firstorder particle filtering algorithm for tracking. In the second part of the paper, we introduce extensive applications of particle filtering in visual tracking. Experimental results show the superior performance of particle filter estimation as a tool for visual tracking.
منابع مشابه
Markov chain Monte Carlo methods for visual tracking
Tracking articulated figures in high dimensional state spaces require tractable methods for inferring posterior distributions of joint locations, angles, and occlusion parameters. Markov chain Monte Carlo (MCMC) methods are efficient sampling procedures for approximating probability distributions. We apply MCMC to the domain of people tracking and investigate a general framework for sample-appr...
متن کاملOn-road Visual Vehicle Tracking Using Markov Chain Monte Carlo Particle Filtering with Metropolis Sampling
In this study, a method for vehicle tracking through video analysis based on Markov chain Monte Carlo (MCMC) particle filtering with metropolis sampling is proposed. The method handles multiple targets with low computational requirements and is, therefore, ideally suited for advanced-driver assistance systems that involve real-time operation. The method exploits the removed perspective domain g...
متن کاملPopulation Based Particle Filtering
This paper proposes a novel particle filtering strategy by combining population Monte Carlo Markov chain methods with sequential Monte Carlo chain particle which we call evolving population Monte Carlo Markov Chain (EP MCMC) filtering. Iterative convergence on groups of particles (populations) is obtained using a specified kernel moving particles toward more likely regions. The proposed techniq...
متن کاملStudy on Multi-Target Tracking Based on Particle Filter Algorithm
Particle filter is a probability estimation method based on Bayesian framework and it has unique advantage to describe the target tracking non-linear and non-Gaussian. In this study, firstly, analyses the particle degeneracy and sample impoverishment in particle filter multi-target tracking algorithm and secondly, it applies Markov Chain Monte Carlo (MCMC) method to improve re-sampling process ...
متن کاملAn MCMC-based Particle Filter for Tracking Target in Glint Noise Environment
In radar tracking application, the observation noise is highly non-Gaussian, which is also referred as glint noise. The performance of extended Kalman filter degrades severely in the presence of glint noise. In this paper, an improved particle filter, Markov chain Monte Carlo particle filter (MCMC-PF), is introduced to cope with radar target tracking in glint noise environment. The Monte Carlo ...
متن کامل