Robust Object Tracking Using Valid Fragments Selection.
Robust Object Tracking Using Valid Fragments Selection.
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DOI：
10.1007/978-3-319-27671-7_62
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发表时间：
2016
期刊：
影响因子：
--
通讯作者：
Luo G
中科院分区：
文献类型：
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作者：
Zheng J;Li B;Tian P;Luo G
局部特征被广泛应用于视觉跟踪中，以提高在部分遮挡、变形和旋转情况下的鲁棒性。提出了一种基于局部碎片的目标跟踪算法。与现有的基于片段的算法不同，该方法首先定义了局部片段的区分度和唯一性，并建立了一个自动预选有用片段的机制。然后，使用Harris-SIFT滤波器来选择当前有效片段，排除遮挡或高度变形的片段。基于这些有效的片段，基于片段的颜色直方图提供了一个结构化的和有效的描述的对象。最后，使用一个有效的片段模板结合位移约束和每个有效片段的相似性来跟踪目标。通过融合特征相似性和有效片段更新目标模板，具有尺度自适应性和对部分遮挡的鲁棒性。实验结果表明，该算法在复杂场景下具有较好的准确性和鲁棒性.
Local features are widely used in visual tracking to improve robustness in cases of partial occlusion, deformation and rotation. This paper proposes a local fragment-based object tracking algorithm. Unlike many existing fragment-based algorithms that allocate the weights to each fragment, this method firstly defines discrimination and uniqueness for local fragment, and builds an automatic pre-selection of useful fragments for tracking. Then, a Harris-SIFT filter is used to choose the current valid fragments, excluding occluded or highly deformed fragments. Based on those valid fragments, fragment-based color histogram provides a structured and effective description for the object. Finally, the object is tracked using a valid fragment template combining the displacement constraint and similarity of each valid fragment. The object template is updated by fusing feature similarity and valid fragments, which is scale-adaptive and robust to partial occlusion. The experimental results show that the proposed algorithm is accurate and robust in challenging scenarios.