DocumentCode :
3585467
Title :
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
Link To Document :
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