A Survey: Target Tracking Algorithm Based on Sparse Representation

Author

Dan Lu ; Linsheng Li ; Qingsen Yan

Author_Institution

Inst. of Digital Media & Commun., Taiyuan Univ. of Sci. & Technol., Taiyuan, China

Volume

2

fYear

2014

Firstpage

195

Lastpage

199

Abstract

In this paper, we introduce the development of object tracking. In particular, we introduce several kinds of target tracking algorithm based on sparse coding, including a robust visual tracking method by casting tracking as a sparse approximation problem in a particle filter framework, kernel sparse tracking with compressive sensing, and real-time compressive tracking. Show the concept of sparse representation and compressed sensing, analyze the meaning of the sparse representation in the target tracking, and compare the algorithm.

Keywords

approximation theory; compressed sensing; computer vision; image representation; object tracking; particle filtering (numerical methods); target tracking; compressive sensing; kernel sparse tracking; object tracking development; particle filter framework; realtime compressive tracking; sparse approximation problem; sparse coding; sparse representation; target tracking algorithm; Compressed sensing; Image coding; Kernel; Pattern recognition; Robustness; Target tracking; Visualization; compressive sensing; compressive tracking; kernel function; l1-Minimization; sparse representation;

fLanguage

English

Publisher

ieee

Conference_Titel

Computational Intelligence and Design (ISCID), 2014 Seventh International Symposium on

Print_ISBN

978-1-4799-7004-9

Type

conf

DOI

10.1109/ISCID.2014.114

Filename

7081969