MULTIPLE OBJECT TRACKING USING INCREMENTAL LEARNING FOR APPEARANCE MODEL ADAPTATION
Franz Pernkopf · 2008
Recently, much work has been devoted to multiple object tracking on the one hand and to appearance model adaptation for a single object tracker on the other side. In this paper, we do both tracking of multiple objects (faces of people) in a meeting scenario and on-line learning to incrementally update the models of the tracked objects to account for appearance changes during tracking. Additionally, we automatically initialize and terminate tracking of individual objects based on low-level features, i.e. face color, face size, and object movement. For tracking a particle filter is incorporated to propagate sample distributions over time. Numerous experiments on meeting data demonstrate the capabilities of our tracking approach. Additionally, we provide an empirical verification of appearance model learning during tracking of an outdoor scene which supports a more robust tracking.