Online Random Ferns for robust visual tracking

Cong Rao, Cong Yao, Xiang Bai, Weichao Qiu, Wenyu Liu

Abstract: Recently many appearance based visual tracking algorithms have been investigated, aimed at building robust appearance models against challenges brought by the varying appearance of the target as well as the unconstrained environment. More often adaptive appearance models were used to capture these variances over time, but this may sometimes result in losing the target (drifting) due to inappropriate update of the model. In this paper an online form of Random Ferns classifier is proposed to accomplish the task of robust appearance modeling with a constrained updating strategy against the potential incorrect update induced by runtime noise. Experiments on challenging benchmark video sequences have been conducted and improvement is observed when compared with recent state-of-the-art algorithms.