Visual Tracking by Sparse Representation and Global Measure
Visual Tracking by Sparse Representation and Global Measure
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DOI：
10.12733/jics20104755
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发表时间：
2014-08
期刊：
影响因子：
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通讯作者：
Meihua Wang;FuMing Liu;Yun Liang
中科院分区：
文献类型：
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作者：
Meihua Wang;FuMing Liu;Yun Liang
虽然已经提出了许多视觉跟踪方法，但诸如严重变形、背景杂乱和严重遮挡等挑战仍然没有得到很好的解决。提出了一种结合稀疏表示和全局测度的视觉跟踪方法。我们首先从目标矩形及其周围的背景的补丁稀疏表示构建外观模型。利用该外观模型，我们成功地识别出了严重遮挡或严重变形的目标斑块。然后，我们定义了一个全局的基础上的显着性和颜色对比度的线索来检测跟踪对象。该方法能很好地区分目标和背景，尤其是在复杂背景中。在不同的挑战图像序列上的实验表明，该方法具有更好的鲁棒性和跟踪稳定性。
While many visual tracking methods have been proposed, the challenges such as severe deformation, cluttered background and heavy occlusion are still under well solved. This paper proposed a new visual tracking method by combing the sparse representation and global measure. We flrst construct an appearance model by sparse representation with the patches from target rectangles and their surrounding backgrounds. With this appearance model, we successfully identify the patches of target undergoing heavy occlusion or severe deformation. Then, we deflne a global measure based on the cues of saliency and color contrast to detect the tracking object. This measure performs well in distinguishing object from background especially in cluttered background. Many experiments on difierent challenge image sequences demonstrate that our method is much more robust and stable in tracking.