Contour Tracking by Stochastic Propagation of Conditional Density
ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume I - Volume I
Structure from Motion Using Sequential Monte Carlo Methods
International Journal of Computer Vision
A Bayesian approach to tracking multiple targets using sensorarrays and particle filters
IEEE Transactions on Signal Processing
Algorithm and architecture for object tracking using particle filter
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
Design and implementation of embedded computer vision systems based on particle filters
Computer Vision and Image Understanding
Algorithm and Parallel Implementation of Particle Filtering and its Use in Waveform-Agile Sensing
Journal of Signal Processing Systems
A self-adaptive heterogeneous multi-core architecture for embedded real-time video object tracking
Journal of Real-Time Image Processing
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In this paper we present algorithmic and architectural methodology for building Particle Filters in hardware. Particle filtering is a new paradigm for filtering in presence of non-Gaussian non-linear state evolution and observation models. This technique has found wide-spread application in tracking, navigation, detection problems especially in a sensing environment. So far most particle filtering implementations are not lucrative for real time problems due to excessive computational complexity involved. In this paper, we re-derive the particle filtering theory to make it more amenable to simplified VLSI implementations. Furthermore, we present and analyze pipelined architectural methodology for designing these computational blocks. Finally, we present an application using the Bearing Only Tracking Problem and evaluate the proposed architecture and algorithmic methodology.