Bilateral Learning for Color Based Tracking

Y. Ren, C.-S. Chua, and Y. Wang (Singapore)


Color model adaptation, Bilateral Learning (BL), spatial information, color-based tracking.


This paper addresses the issue of color model learning and adaptation when color is used as a feature for object tracking in a dynamic scene. Under different environmental conditions, e.g. illumination changes or non stationary scenes, a static color model is inadequate and color model adaptation is required. The color model adaptation for object tracking can be classified as an unsupervised (or semi-supervised) learning problem. The algorithm should be able to label the data as the target or the background, and select the reliable training samples to update the color model automatically. A Bilateral Learning (BL) approach is proposed in this paper. The spatial and color information of the target are combined in the color model adaptation and color based object tracking procedure. The color model and spatial model are adapted in the color space and image space alternatively, which results in the color model adaptation and the localization of the target along the image sequence. Experimental results show the effectiveness and efficacy of the proposed method for the color model adaptation and object tracking under illumination changes and environmental noises.

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