Work

Feature Selection Tracking Algorithm Based on Sparse Representation

Year: 2015

Type: article

Abstract: 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.

Source: Mathematical Problems in Engineering

DOI: 10.1155/2015/684370

Authors: Hui-dong Lou, Wei-guang Li, Yue-en Hou, Qing-he Yao, Guo-qiang Ye, Hao Wan

Institutions: South China University of Technology, Jiaying University, Sun Yat-sen University

Language: English

Topic: Video Surveillance and Tracking Methods

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $2,550

OpenAlex ID: W1954827437