首页> 外文会议>International Conference on Image Analysis and Processing(ICIAP 2005); 20050906-08; Cagliari(IT) >Track Matching by Major Color Histograms Matching and Post-matching Integration
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Track Matching by Major Color Histograms Matching and Post-matching Integration

机译:通过主要颜色直方图匹配和匹配后整合进行轨迹匹配

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In this paper we present a track matching algorithm based on the "major color" histograms matching and the post-matching integration useful for tracking a single object across multiple, limitedly disjoint cameras. First, the Major Color Spectrum Histogram (MCSH) is introduced to represent a moving object in a single frame by its most frequent colors only. Then, a two-directional similarity measurement based on the MCHS is used to measure the similarity of any two given moving objects in single frames. Finally, our track matching algorithm extends the single-frame matching along the objects' tracks by a post-matching integration algorithm. Experimental results presented in this paper show the accuracy of the proposed track matching algorithm: the similarity of two tracks from the same moving objects has proved as high as 95%, while the similarity of two tracks from different moving objects has been kept as low as up to 28%. The post-matching integration step proves able to remove detailed errors occurring at the frame level, thus making track matching more robust and reliable.
机译:在本文中,我们提出了一种基于“主要颜色”直方图匹配和匹配后集成的跟踪匹配算法,可用于跨多个有限不相交的摄像机跟踪单个对象。首先,引入主要光谱直方图(MCSH)来仅通过其最频繁的颜色来表示单个帧中的移动对象。然后,基于MCHS的双向相似度测量用于测量单个帧中任意两个给定运动对象的相似度。最后,我们的轨迹匹配算法通过后匹配集成算法沿对象的轨迹扩展了单帧匹配。本文提出的实验结果证明了所提出的轨迹匹配算法的准确性:来自同一运动对象的两条轨迹的相似度已被证明高达95%,而来自不同运动对象的两条轨迹的相似度却一直保持在低水平。高达28%。匹配后集成步骤证明能够消除在帧级别发生的详细错误,从而使轨道匹配更加健壮和可靠。

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