A novel occlusion handling method based on particle filtering for visual tracking
Liangqun Li, Chunlan Li, Junbin Liu, Weixin Xie · 2016
Although visual tracking have been greatly improved in the last decade, there are still many challenges that are not fully resolved. Of these challenges, occlusions, which can be long lasting, are often ignored. Under the framework of particle filtering, this paper uses the incremental principal component analysis subspace method to learn an orthogonal subspace, then gets the linear representation of target appearance. To avoid the tracking drift, an occlusion handling scheme was proposed. In this scheme, firstly, determinate state of the target by the variance of the reconstruction error, then predict the position of the target by velocity prediction method and particle filtering method. The experiments revealed that proposed algorithm is impactful to deal with occlusion.