Robust Visual Tracking with Improved Subspace Representation Model
Abstract: In this paper, we
propose a robust visual tracking with an improved subspace representation model.
Different from traditional subspace representation model, we use sparse
representation, but not the collaborative representation to reconstruct the
observation samples, which can avoid the redundant object features in subspace
effectively. Moreover, to reject the outliers in the process of tracking, we
also propose the combination of sparse box templates and Laplacian residual. To
solve the minimization problem of object representation efficiently, a fast
numerical algorithm that accelerated proximal gradient (APG) approach is
proposed for the Lagrangian function. Finally, experimental results on several
challenging video sequences show better performance than LSST and many
state-of-the-art trackers.
Author: Jing Cheng, Sucheng
Kang
Journal Code: jptkomputergg170154