Title :
A sequential Monte Carlo method for particle filters
Author :
Gao, Hongzhi ; Green, Richard
Author_Institution :
Dept. of Comput. Sci. & Software Eng., Univ. of Canterbury, Christchurch
Abstract :
An object oriented particle filter framework is proposed based on sequential Monte Carlo methods. Particle filter is an extensively used algorithm for vision based tracking systems. However, little work has been done in the past literature to investigate the implementation strategies of the particle filter algorithm. In this paper, we propose a framework based on open source particle filter libraries and evaluate respective advantages and disadvantages. The results support the proposed object oriented particle filter being a most useful tool for computer vision based stochastic prediction.
Keywords :
Monte Carlo methods; computer vision; particle filtering (numerical methods); computer vision; particle filters; sequential Monte Carlo method; stochastic prediction; vision-based tracking systems; Application software; Computer science; Computer vision; Machine vision; Particle filters; Particle tracking; Software engineering; Software libraries; State-space methods; Stochastic processes; application framework; implementation strategy; particle filter;
Conference_Titel :
Image and Vision Computing New Zealand, 2008. IVCNZ 2008. 23rd International Conference
Conference_Location :
Christchurch
Print_ISBN :
978-1-4244-3780-1
Electronic_ISBN :
978-1-4244-2583-9
DOI :
10.1109/IVCNZ.2008.4762108
