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To reduce the tracking errors caused by high-speed motion and variable motion in the process of moving target tracking, a novel Mean-Shift tracking algorithm based on Kalman filter using adaptive window and sub-blocking is proposed in this paper. Moving target's utmost position is predicted by combining Kalman filter and historical information, which is used as the initial position. During describing...
This paper introduces an algorithm to automatically and continuously select the most appropriate color space to use in order to improve the performances of visual tracking. Eight color spaces are tested, and the Mean-Shift (MS) tracker is considered. The selection of the colorspace is made using an evaluation criterion based on the quality of the weights involved in the MS tracking, and implicitly...
Mean-shift algorithm is one of the well-known tracking algorithms because of its robust performance. However, Mean-shift algorithm tracks targets only by the color or intensity features. That is to say that, Mean-shift can only tracking the statistical features of pixels. The universal Mean-shift which can track any features of targets has not been developed. We propose a strategy which does not need...
To improve the limitation of Mean-Shift lack of the template update, an algorithm based on mixture Gaussian model is proposed. It treats the target region as ldquobackgroundrdquo, and three Gaussian functions are used to evaluate each pixel value in the target region. After using Mean-Shift algorithm to track the target region in the current frame, we update the Mixture Gaussian Model with the new...
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