【資料名稱】：Online Selecting Discriminative Tracking

【資料作者】：Jianyu Wang，Xilin Chen，Wen Gao

【資料日期】：2005

【資料語(yǔ)言】：英文

【資料格式】：PDF

【資料目錄和簡(jiǎn)介】：

Background clutter is a big challenge to the
performance of a visual tracker. The paper proposes a
method to keep the tracker robust to background
clutters by online selecting discriminative features
from a large feature space. Furthermore, the feature
selection procedure is embedded into the particle
filtering process with the aid of existed “background”
particles. Feature values from background patches and
object observations are sampled during tracking and
Fisher discriminant is employed to rank the
classification capacity ofeach feature based on
sampled values. Top-ranked discriminative features
are selected into the appearance model and
simultaneously invalid features are removed out to
adjust the object representation adaptively. The
implemented tracker with online discriminative feature
selection module embedded shows promising results on
experimental video sequences.