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Project: Object Tracking using Particle filtering

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Particle filter is a method to implement a Bayesian inference filter using Monte Carlo Simulation. It is well known that the Kalman filter provides an analytically optimal Bayesian solution for the linear/Gaussian case. Particle filter is more general, and can model non-linear/non-Gaussian case. Particle filter gained popularity for object tracking because it was introduced as the Condensation algorithm for object tracking in the computer vision community.

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Tag: ComputerVision ParticleFilter ConDensation C++