A Comparison of Mean Shift Tracking Methods∗
نویسنده
چکیده
The mean shift algorithm is a well-known statistical method for finding local maxima in probability distributions. Besides filtering and segmentation it is applied in the field of object tracking. There are several approaches that use the mean shift method for locating target objects in video sequences. This paper compares three similar approaches and investigates their performance on different test videos.
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The mean shift algorithm is one of the popular methods in visual tracking for non-rigid moving targets. Basically, it is able to locate repeatedly the central mode of a desirable target. Object representation in mean shift algorithm is based on its feature histogram within a non-oriented individual kernel mask. Truly, adjusting of the kernel scale is the most critical challenge in this method. ...
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