Tracking of Multiple Targets Using On-Line Learning for Appearance Model Adaptation
Franz Pernkopf · 2008
We propose visual tracking of multiple objects (faces of people) in a meeting scenario based on low-level features such as skin-color, target motion, and target size. Based on these features automatic initialization and termination of objects is performed. Furthermore, on-line learning is used to incrementally update the models of the tracked objects to reflect the appearance changes. For tracking a particle filter is incorporated to propagate sample distributions over time. We discuss the close relationship between our implemented tracker based on particle filters and genetic algorithms. Numerous experiments on meeting data demonstrate the capabilities of our tracking approach.