Mean shift, a simple iterative procedure that shifts each data point to the average of data points in its neighborhood, is generalized and analyzed in this paper. This generalization makes some k-means like clustering algorithms its special cases. It is shown that mean shift is a mode-seeking process on a surface constructed with a “shadow” kernel. For Gaussian kernels, mean shift is a gradient...

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...

The original mean shift algorithm [1] on Euclidean spaces (MS) was extended in [2] to operate on general Riemannian manifolds. This extension is extrinsic (Ext-MS) since the mode seeking is performed on the tangent spaces [3], where the underlying curvature is not fully considered (tangent spaces are only valid in a small neighborhood). In [3] was proposed an intrinsic mean shift designed to op...

Mean shift is an iterative mode-seeking algorithm widely used in pattern recognition and computer vision. However, its convergence is sometimes too slow to be practical. In this paper, we improve the convergence speed of mean shift by dynamically updating the sample set during the iterations, and the resultant procedure is called dynamic mean shift (DMS). When the data is locally Gaussian, it c...

Object tracking algorithms, when it comes to implementing it on hardware ASIC, it becomes difficult task, due to certain limitations in hardware. This paper shows how mean-shift algorithm is implemented in HDL along with the description of ports and interfaces. Keywords— Object tracking, complexity in hardware ASIC, Mean Shift algorithm, Histogram, Bhattacharya coefficient

Mean-Shift tracking is a popular algorithm for object tracking since it is easy to implement and it is fast and robust. In this paper, we address the problem of scale adaptation of the Hellinger distance based Mean-Shift tracker. We start from a theoretical derivation of scale estimation in the Mean-Shift framework. To make the scale estimation robust and suitable for tracking, we introduce reg...

To settle out the problem that search of speaker change point (SCP) is blind and exhaustive, mean shift is proposed to seek SCP by estimating the kernel density of speech stream in this paper. It contains three steps: seeking peak points using mean shift firstly, using maximum likelihood ratio (MLR) to compute the MLR value of the peak points secondly, and seeking SCPs from MLR value using the ...

The mean-shift algorithm is an efficient technique for tracking 2D blobs through an image. Although the scale of the mean-shift kernel is a crucial parameter, there is presently no clean mechanism for choosing this scale or updating it while tracking blobs that are changing in size. In this paper, we adapt Lindeberg’s theory of feature scale selection based on local maxima of differential scale...