Discriminative subspace learning with sparse representation view-based model for robust visual tracking

作者：

Highlights：

• A tracker combines the generative and the discriminative model.

• Introduce maximum margin projection into object tracking.

• Transform MMP function to be solved under the Spectral Regression framework.

• Learning an incremental view-based model of the target with sparse representation.

• Experiments show our tracker to be more robust and stable than state-of-the-art methods.

摘要

Highlights•A tracker combines the generative and the discriminative model.•Introduce maximum margin projection into object tracking.•Transform MMP function to be solved under the Spectral Regression framework.•Learning an incremental view-based model of the target with sparse representation.•Experiments show our tracker to be more robust and stable than state-of-the-art methods.

论文关键词：Discriminative subspace learning,Spectral regression,Sparse representation,Object tracking

论文评审过程：Received 22 June 2012, Revised 5 May 2013, Accepted 16 July 2013, Available online 2 August 2013.

论文官网地址：https://doi.org/10.1016/j.patcog.2013.07.010