Appearance tracking using adaptive models in a particle filter

The particle filter is a popular tool for visual tracking. Usually, the appearance model is eitherfixed or rapidly changing and the motion model is simply a random walk with fixed noise vari-
ance. Also, the number of particles used is typically fixed. All these factors make the visual
tracker unstable. To stabilize the tracker, we propose the following measures: an observation
model arising from an adaptive noise variance, and adaptive number of particles. The adaptive-
velocity is computed via a first-order linear predictor using the previous particle configuration.
Tracking under occlusion is accomplished using robust statistics. Experimental results on track-
ing visual objects in long video sequences such as vehicles, tank, and human faces demonstrate
the effectiveness and robustness of our algorithm.