K-means tracker : A multiple colors object tracking algorithm

概要

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This paper presents a K-means tracker, which is a novel visual tracking algorithm. This algorithm is robust against the interfused background, because it discriminates "target" pixels from "background" pixels in the tracking region by applying K-means clustering to both the positive and negative information of the target. To ensure the robustness of this algorithm, we apply the following ideas : 1) To represent the color similarity and spatial approximation of the target simultaneously, we use a 5D feature vector consisting of the position (x, y) and color (y, u, v) information for object tracking. The object tracking is performed and updated not only in the image space but also in the color space at the same time. Therefore, this adaptive nature guarantees our method to be robust against the target color change. 2) By using a variable ellipse model to restrict the target search area and represent the non-target pixels surrounding the target, the algorithm can cope with the changes of the scale and shape of the target object flexibly. 3) Even the tracking sometimes fails, this algorithm can automatically discover and recover from the tracking failure based on the positive and negative information. To capture motion-blur-free images of a moving object at video rate, we control a set of active cameras which are mounted on the pan-tilt units according to the results of the K-means tracker.
一般社団法人情報処理学会の論文
2005-11-17