TY - GEN
T1 - A Survey
T2 - 7th International Symposium on Computational Intelligence and Design, ISCID 2014
AU - Lu, Dan
AU - Li, Linsheng
AU - Yan, Qingsen
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2015/4/8
Y1 - 2015/4/8
N2 - 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.
AB - 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.
KW - compressive sensing
KW - compressive tracking
KW - kernel function
KW - l1-Minimization
KW - sparse representation
UR - http://www.scopus.com/inward/record.url?scp=84931066997&partnerID=8YFLogxK
U2 - 10.1109/ISCID.2014.114
DO - 10.1109/ISCID.2014.114
M3 - 会议稿件
AN - SCOPUS:84931066997
T3 - Proceedings - 2014 7th International Symposium on Computational Intelligence and Design, ISCID 2014
SP - 195
EP - 199
BT - Proceedings - 2014 7th International Symposium on Computational Intelligence and Design, ISCID 2014
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 13 December 2014 through 14 December 2014
ER -