Rao-Blackwellized particle filter for Gaussian mixture models and application to visual tracking
One of the most important problems in visual tracking is how to incrementally update the appearance model because the appearance of a target object can be easily changed with time when the target is a deformable object or it is moving under varying illumination conditions. To solve these problems, we present a Rao-Blackwellized particle filter (RBPF)-based object tracking algorithm with the adaptive appearance model represented by a Gaussian mixture model (or a mixture of Gaussians model) because a single Gaussian reveals limitations in modeling the target appearance when observations are corrupted by occlusion or the tracking error. We demonstrate the robustness of the proposed method using well-known databases, such as the CAVIAR and the PETS databases.