@article {12710, title = {Appearance tracking using adaptive models in a particle filter}, journal = {Proc. of 6th Asian Conference on Computer Vision (ACCV)}, year = {2004}, month = {2004///}, abstract = {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. }, author = {Zhou, S. and Chellapa, Rama and Moghaddam, B.} }