A Nonparametric Treatment for Location/Segmentation Based Visual Tracking

In this paper, we address two closely related visual tracking problems: 1) localizing a target\´s position in low or moderate resolution videos and 2) segmenting a target\´s image support in moderate to high resolution videos. Both tasks are treated as an online binary classification problem using dynamic foreground/background appearance models. Our major contribution is a novel nonparametric approach that successfully maintains a temporally changing appearance model for both foreground and background. The appearance models are formulated as "bags of image patches" that approximate the true two-class appearance distributions. They are maintained using a temporal-adaptive importance resampling procedure that is based on simple nonparametric statistics of the appearance patch bags. The overall framework is independent of an specific foreground/background classification process and thus offers the freedom to use different classifiers. We demonstrate the effectiveness of our approach with extensive comparative experimental results on sequences from previous visual tracking and video matting work as well as our own data.