Particle Filter (Mouse Tracker Example)

Particle Filter (evolved after Bayesian) was introduced by Gordon (Gordon et al., 1993), and has been a preferred choice for tracking application due to its ability to solve both non linear equations and non-Gaussian noise. Its main principle is derived from the Sequential Monte Carlo method (Bolic, 2004) that recursively generate random measurements to approximate the distribution of unknowns variables.

The Particle Filter technique has been proved to be robust and is widely used in many applications such as robotics (Bererton, 2004), human tracking (Okuma et al., 2004; Hue et al., 2001; Green and Guan, 2003), network applications (Coates, 2004), vehicle tracking (Nummiaro et al., 2002), sound detection (Checka et al., 2004), bearing tracking (Bolic, 2004), and gesture recognition (Alexander, 2002).

In this page, we show a simple example of how Particle Filter can be used to track mouse movements.