Robust visual tracking algorithm based on bidirectional sparse representation

Wang Bao-Xian;  Zhao Bao-Jun;  Tang Lin-Bo;  Wang Shui-Gen;  Wu Jing-Hui

doi:10.7498/aps.63.234201

Robust visual tracking algorithm based on bidirectional sparse representation

Acta Physica Sinica ◽

10.7498/aps.63.234201 ◽

2014 ◽

Vol 63 (23) ◽

pp. 234201

Author(s):

Wang Bao-Xian ◽

Zhao Bao-Jun ◽

Tang Lin-Bo ◽

Wang Shui-Gen ◽

Wu Jing-Hui

Keyword(s):

Sparse Representation ◽

Visual Tracking ◽

Tracking Algorithm

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Drift-free tracking via the construction of an effective dictionary

International Journal of Advanced Robotic Systems ◽

10.1177/1729881420929651 ◽

2020 ◽

Vol 17 (3) ◽

pp. 172988142092965

Author(s):

Li Zhao ◽

Pengcheng Huang ◽

Fei Liu ◽

Hui Huang ◽

Huiling Chen

Keyword(s):

Sparse Representation ◽

Visual Tracking ◽

Tracking Algorithm ◽

Linear Combinations ◽

Computational Burden ◽

Tracking Algorithms ◽

Tracking Process ◽

Appearance Changes

Template dictionary construction is an important issue in sparse representation (SP)-based tracking algorithms. In this article, a drift-free visual tracking algorithm is proposed via the construction of an effective template dictionary. The constructed dictionary is composed of three categories of atoms (templates): nonpolluted atoms, variational atoms, and noise atoms. Moreover, the linear combinations of nonpolluted atoms are also added to the dictionary for the diversity of atoms. All the atoms are selectively updated to capture appearance changes and alleviate the model drifting problem. A bidirectional tracking process is used and each process is optimized by two-step SP, which greatly reduces the computational burden. Compared with other related works, the constructed dictionary and tracking algorithm are both robust and efficient.

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Feature Selection Tracking Algorithm Based on Sparse Representation

Mathematical Problems in Engineering ◽

10.1155/2015/684370 ◽

2015 ◽

Vol 2015 ◽

pp. 1-9

Author(s):

Hui-dong Lou ◽

Wei-guang Li ◽

Yue-en Hou ◽

Qing-he Yao ◽

Guo-qiang Ye ◽

...

Keyword(s):

Feature Selection ◽

Sparse Representation ◽

Target Tracking ◽

Visual Tracking ◽

Fast Algorithm ◽

The State ◽

Tracking Algorithm ◽

Complex Environment

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.

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