Robust visual tracking with classifier-like appearance model and entropy particle filter
Yu Song, Qingling Li, Deli Yan, Yifei Kang · 2012
The detection based visual tracker treats tracking as the object and its surround background online classification problem. There are two main difficult issues in this method: one is to specify exact labels for the online samples, the other is to avoid template drift that caused by wrong update of the classifier-like appearance model. To overcome the problems, a novel tracking algorithm based on online Multiple Instance Learning (MIL) and entropy particle filter is proposed. Main contributions of our work are: (1) we introduce MIL in particle filter visual tracking framework to reduce the online training error of the classifier-like appearance model; (2) the appearance model consists of an initial fixed MIL classifier and an online dynamic MIL classifier; (3) a particle set maximum negative entropy criterion is designed to online fuse the two classifiers. Experimental results verify the effectiveness of the proposed algorithm.