Algorithmic and Architectural Optimizations for Computationally Efficient Particle Filtering

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...

• Type: Journal
• Year: 2008
• Where: TIP
• Authors: Aswin C. Sankaranarayanan, Ankur Srivastava, Rama Chellappa
• Keywords: Particle Filtering, Particle Filtering Algorithms, Particle Filtering Methods, TIP 2008