In this paper, we brought out a noise-insensitive pixel-wise object tracking algorithm whose kernel is a new reliable data grouping algorithm that introduces the reliability evaluation into the existing K-means clustering (called as RK-means clustering). The RK-means clustering concentrates on two problems of the existing K-mean clustering algorithm: 1) the unreliable clustering result when the noise data exists; 2) the bad/wrong clustering result caused by the incorrectly assumed number of clusters. The first problem is solved by evaluating the reliability of classifying an unknown data vector according to the triangular relationship among it and its two nearest cluster centers. Noise data will be ignored by being assigned low reliability. The second problem is solved by introducing a new group merging method that can delete pairs of ”too near” data groups by checking their variance and average reliability, and then combining them together. We developed a video-rate object tracking system (called as RK-means tracker) with the proposed algorithm. The extensive experiments of tracking various objects in cluttered environments confirmed its effectiveness and advantages.


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    Title :

    A Noise-Insensitive Object Tracking Algorithm


    Contributors:
    Yagi, Yasushi (editor) / Kang, Sing Bing (editor) / Kweon, In So (editor) / Zha, Hongbin (editor) / Hua, Chunsheng (author) / Chen, Qian (author) / Wu, Haiyuan (author) / Wada, Toshikazu (author)

    Conference:

    Asian Conference on Computer Vision ; 2007 ; Tokyo, Japan November 18, 2007 - November 22, 2007


    Published in:

    Computer Vision – ACCV 2007 ; Chapter : 53 ; 565-575


    Publication date :

    2007-01-01


    Size :

    11 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English





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