Feature-vector for the MeanShift
The article gives the description of the feature vector, which is suitable for the MeanShift procedure, uses all the color information of the RGB24 format and has a dimension exceeding only 1.5 times the dimension of the smallest 512-dimensional vector used for the Kernel Based Object Tracking procedure. For the described feature vector, a function of similarity of two elliptical areas of the frame is built. For the similarity function, formulas are found for the gradient vector - the mean shift vector, which indicates the direction of the growth of similarity in four-dimensional space of all elliptical regions covering the object in the frame. Knowing the greatest value of the similarity function of two elliptical regions, the length of the displacement vector in the four-dimensional space of all elliptical regions was found. To this vector the previous point in space must be moved at the current moment, i.e. the values of the coordinates of the center and the dimensions of the ellipse, in order to obtain the best similarity of the current elliptical area from the previous one. Finally, so as to implement Kernel Based Object Tracking, an algorithm of successive iterations (Newton's method) has been developed, which allows finding the parameters of the ellipse that really has the best similarity. The experiments were carried out and their results were presented and discussed.