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Feature Selection Tracking Algorithm Based on Sparse Representation

机译：基于稀疏表示的特征选择跟踪算法

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摘要

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.
机译：为了提高视觉跟踪算法在复杂环境中的鲁棒性，提出了一种基于多特征选择和稀疏表示的视觉跟踪算法。在粒子过滤器的框架中，具有低目标相似性的粒子首先通过快速算法被过滤掉。然后，基于稀疏重构样本标签的原理，在计算中要考虑到背景差异较大的特征，以减少遮挡和噪声的干扰。最后，通过稀疏表示线性重构候选目标，并通过APG方法求解稀疏方程，获得目标状态。四个对比实验表明，本文提出的算法有效地提高了目标跟踪算法的鲁棒性。

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  • 来源
    《Mathematical Problems in Engineering》 |2015年第21期|684370.1-684370.9|共9页
  • 作者

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

  • 作者单位

    S China Univ Technol, Sch Mech & Automot Engn, Guangzhou, Guangdong, Peoples R China;

    S China Univ Technol, Sch Mech & Automot Engn, Guangzhou, Guangdong, Peoples R China;

    Jiaying Univ, Sch Comp Sci, Meizhou, Guangdong, Peoples R China;

    Sun Yat Sen Univ, Sch Engn, Guangzhou 510275, Guangdong, Peoples R China;

    S China Univ Technol, Sch Mech & Automot Engn, Guangzhou, Guangdong, Peoples R China;

    S China Univ Technol, Sch Mech & Automot Engn, Guangzhou, Guangdong, Peoples R China;

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