Object tracking using robust subspace learning in particle filter
基于鲁棒子空间学习的粒子滤波跟踪算法*

Observing that low-dimensional linear subspaces are able to effectively model image variation caused by illumination and pose change.While most related tracking algorithms use a pre-trained view-based eigenspace representation,it is imperative to collect a large set of training images covering the range of possible appearance variation,and this representation,once learned,is not updated.This paper presented a tracking method that incrementally learned a low-dimensional subspace representation,efficiently ad...