Abstract — The many different mean shift techniques for object tracking in real time are discussed in this paper. The mean shift is a non-parametric feature space analysis technique. It is a method for finding local maxima of a density function from given discrete data samples.There are several

for target detection in videos taken by Unmanned Aerial Vehicle. Traditional Mean shift tracking technique works well for dynamic objects with static background but UAV videos are of dynamic nature. The target may increase or decrease in size invariantly in consequent frames. Thus the proposed technique

A general nonparametric technique is proposed for the analysis of a complex multimodal feature space and to delineate arbitrarily shaped clusters in it. The basic computational module of the technique is an old pattern recognition procedure, the mean shift. We prove for discrete data

The Hough Transform is a well known robust technique to infer shapes from a set of spatial points

functions suitable for gradient-based optimization, hence, the target localization problem can be formulated using the basin of attraction of the local maxima. We employ a metric derived from the Bhattacharyya coefficient as similarity measure, and use the mean shift procedure to perform the optimization

local binary MRF. We propose here a new TS-MRF unsupervised segmen-tation technique which improves upon the original algorithm by selecting a better tree structure and eliminating spurious classes. Such results are obtained by using the Mean-Shift procedure to estimate the number of pdf modes at each

Abstract We present two solutions for the scale selection problem in computer vision. The first one is completely nonparametric and is based on the the adaptive estimation of the normalized density gradient. Employing the sample point estimator, we define the Variable Bandwidth Mean Shift, prove

binary MRF. We propose here a new TS-MRF unsupervised segmentation technique which improves upon the original algorithm w.r.t. several critical issues, from the selection of a suitable tree structure to the elimination of spurious classes. The major improvements come from resorting to the Mean-Shift

Mean-shift analysis is a general nonparametric clustering technique based on density estimation for the analysis of complex feature spaces. The algorithm consists of a simple iterative procedure that shifts each of the feature points to the nearest stationary point along the gradient directions

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 or updating scale while tracking blobs that are changing in size. We adapt Lindeberg