Design and implementation of embedded computer vision systems based on particle filters
Computer Vision and Image Understanding
Easy-hardware-implementation MMPF for Maneuvering Target Tracking: Algorithm and Architecture
Journal of Signal Processing Systems
Algorithm and Parallel Implementation of Particle Filtering and its Use in Waveform-Agile Sensing
Journal of Signal Processing Systems
Hierarchical Resampling Algorithm and Architecture for Distributed Particle Filters
Journal of Signal Processing Systems
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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.