Feature Selection Tracking Algorithm Based on Sparse Representation
In order to enhance the robustness of visual tracking algorithm in complex environment, a novel visual tracking algorithm based on multifeature selection and sparse representation is proposed. In the framework of particles filter, particles with low target similarity are first filtered out by a fast...
Main Authors: | , , , , , |
---|---|
Format: | Article |
Language: | English |
Published: |
Hindawi Limited
2015-01-01
|
Series: | Mathematical Problems in Engineering |
Online Access: | http://dx.doi.org/10.1155/2015/684370 |
id |
doaj-96d40ebfb38744fd9268f1d9101dc909 |
---|---|
record_format |
Article |
spelling |
doaj-96d40ebfb38744fd9268f1d9101dc9092020-11-24T22:55:16ZengHindawi LimitedMathematical Problems in Engineering1024-123X1563-51472015-01-01201510.1155/2015/684370684370Feature Selection Tracking Algorithm Based on Sparse RepresentationHui-dong Lou0Wei-guang Li1Yue-en Hou2Qing-he Yao3Guo-qiang Ye4Hao Wan5School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou, Guangdong, ChinaSchool of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou, Guangdong, ChinaSchool of Computer Science, Jiaying University, Meizhou, Guangdong, ChinaSchool of Engineering, Sun Yat-sen University, Guangzhou, Guangdong, ChinaSchool of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou, Guangdong, ChinaSchool of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou, Guangdong, ChinaIn order to enhance the robustness of visual tracking algorithm in complex environment, a novel visual tracking algorithm based on multifeature selection and sparse representation is proposed. In the framework of particles filter, particles with low target similarity are first filtered out by a fast algorithm; then, based on the principle of sparsely reconstructing the sample label, the features with high differentiation against the background are involved in the computation so as to reduce the disturbance of occlusions and noises. Finally, candidate targets are linearly reconstructed via sparse representation and the sparse equation is solved by using APG method to obtain the state of the target. Four comparative experiments demonstrate that the proposed algorithm in this paper has effectively improved the robustness of the target tracking algorithm.http://dx.doi.org/10.1155/2015/684370 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Hui-dong Lou Wei-guang Li Yue-en Hou Qing-he Yao Guo-qiang Ye Hao Wan |
spellingShingle |
Hui-dong Lou Wei-guang Li Yue-en Hou Qing-he Yao Guo-qiang Ye Hao Wan Feature Selection Tracking Algorithm Based on Sparse Representation Mathematical Problems in Engineering |
author_facet |
Hui-dong Lou Wei-guang Li Yue-en Hou Qing-he Yao Guo-qiang Ye Hao Wan |
author_sort |
Hui-dong Lou |
title |
Feature Selection Tracking Algorithm Based on Sparse Representation |
title_short |
Feature Selection Tracking Algorithm Based on Sparse Representation |
title_full |
Feature Selection Tracking Algorithm Based on Sparse Representation |
title_fullStr |
Feature Selection Tracking Algorithm Based on Sparse Representation |
title_full_unstemmed |
Feature Selection Tracking Algorithm Based on Sparse Representation |
title_sort |
feature selection tracking algorithm based on sparse representation |
publisher |
Hindawi Limited |
series |
Mathematical Problems in Engineering |
issn |
1024-123X 1563-5147 |
publishDate |
2015-01-01 |
description |
In order to enhance the robustness of visual tracking algorithm in complex environment, a novel visual tracking algorithm based on multifeature selection and sparse representation is proposed. In the framework of particles filter, particles with low target similarity are first filtered out by a fast algorithm; then, based on the principle of sparsely reconstructing the sample label, the features with high differentiation against the background are involved in the computation so as to reduce the disturbance of occlusions and noises. Finally, candidate targets are linearly reconstructed via sparse representation and the sparse equation is solved by using APG method to obtain the state of the target. Four comparative experiments demonstrate that the proposed algorithm in this paper has effectively improved the robustness of the target tracking algorithm. |
url |
http://dx.doi.org/10.1155/2015/684370 |
work_keys_str_mv |
AT huidonglou featureselectiontrackingalgorithmbasedonsparserepresentation AT weiguangli featureselectiontrackingalgorithmbasedonsparserepresentation AT yueenhou featureselectiontrackingalgorithmbasedonsparserepresentation AT qingheyao featureselectiontrackingalgorithmbasedonsparserepresentation AT guoqiangye featureselectiontrackingalgorithmbasedonsparserepresentation AT haowan featureselectiontrackingalgorithmbasedonsparserepresentation |
_version_ |
1725657145951649792 |