A feature guided particle filter for robust hand tracking

Particle filtering offers an interesting framework for visual tracking. Unlike the Kalman filter, particle filters can deal with non-linear and non-Gaussian problems, which makes them suitable for visual tracking in presence of real-life disturbance factors, such as background clutter and movement, fast and unpredictable object movement and unideal illumination conditions. This paper presents a robust hand tracking particle filter algorithm which exploits the principle of importance sampling with a novel proposal distribution. The proposal distribution is based on effectively calculated color blob features, propagating the particles robustly through time even in unideal conditions. In addition, a novel method for conditional color model adaptation is proposed. The experiments show that using these methods in the particle filtering framework enables hand tracking with fast movements under real world conditions.