K-means clustering based pixel-wise object tracking (コンピュータビジョンとイメージメディア)

概要

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This paper brings out a robust pixel-wise object tracking algorithm which is based on the K-means clustering. In this paper, the target object is assumed to be non-rigid and may contain apertures. In order to achieve the robust object tracking against such objects, several ideas are applied in this work: 1) Pixel-wise clustering algorithm is applied for tracking the non-rigid object and removing the mixed background pixels from the search area; 2) Embedding the negative samples into K-means clustering so as to achieve the adaptive pixel classification without the fixed threshold; 3) Representing the image feature with a color-position feature vector so that this algorithm can follow the changes of target colors and position simultaneously; 4) A variable ellipse model is used to restrict the search area and represent the surrounding background samples; 5) Tracking failure detection and recovery processes are brought out according to both the target and background samples; 6) A radial sampling method is brought out not only for speeding up the clustering process but also improving the robustness of this algorithm. We have set up a video-rate object tracking system with the proposed algorithm. Through extensive experiments, the effectiveness and advantages of this K-means clustering based tracking algorithm are confirmed.
一般社団法人情報処理学会の論文
2007-05-14