Algorithmic and Architectural Optimizations for Computationally Efficient Particle Filtering
Sankaranarayanan, A.C.
Srivastava, A.
Chellappa, R.
Univ. of Maryland, College Park;
This paper appears in: Image Processing, IEEE Transactions on
Publication Date: May 2008
Volume: 17,
Issue: 5
On page(s): 737-748
ISSN: 1057-7149
INSPEC Accession Number: 9921203
Digital Object Identifier: 10.1109/TIP.2008.920760
First Published: 2008-03-31
Current Version Published: 2008-04-08
Abstract
In this paper, we analyze the computational challenges in implementing particle filtering, especially to video sequences. Particle filtering is a technique used for filtering nonlinear dynamical systems driven by non-Gaussian noise processes. It has found widespread applications in detection, navigation, and tracking problems. Although, in general, particle filtering methods yield improved results, it is difficult to achieve real time performance. In this paper, we analyze the computational drawbacks of traditional particle filtering algorithms, and present a method for implementing the particle filter using the Independent Metropolis Hastings sampler, that is highly amenable to pipelined implementations and parallelization. We analyze the implementations of the proposed algorithm, and, in particular, concentrate on implementations that have minimum processing times. It is shown that the design parameters for the fastest implementation can be chosen by solving a set of convex programs. The proposed computational methodology was verified using a cluster of PCs for the application of visual tracking. We demonstrate a linear speedup of the algorithm using the methodology proposed in the paper.
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