@inproceedings{badb8a0f0a2440e185bded791d7a57a9,
title = "A Survey: Target Tracking Algorithm Based on Sparse Representation",
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 = "compressive sensing, compressive tracking, kernel function, l1-Minimization, sparse representation",
author = "Dan Lu and Linsheng Li and Qingsen Yan",
note = "Publisher Copyright: {\textcopyright} 2014 IEEE.; 7th International Symposium on Computational Intelligence and Design, ISCID 2014 ; Conference date: 13-12-2014 Through 14-12-2014",
year = "2015",
month = apr,
day = "8",
doi = "10.1109/ISCID.2014.114",
language = "英语",
series = "Proceedings - 2014 7th International Symposium on Computational Intelligence and Design, ISCID 2014",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "195--199",
booktitle = "Proceedings - 2014 7th International Symposium on Computational Intelligence and Design, ISCID 2014",
}