This paper presents a new approach to the real time

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Unformatted text preview: paper presents a new approach to the real-time tracking of non-rigid objects based on visual features such as color and/or texture, whose statistical distributions characterize the object of interest. The proposed tracking is appropriate for a large variety of objects with di erent color/texture patterns, being robust to partial occlusions, clutter, rotation in depth, and changes in camera position. It is a natural application to motion analysis of the mean shift procedure introduced earlier 6, 7]. The mean shift iterations are employed to nd the target candidate that is the most similar to a given target model, with the similarity being expressed by a metric based on the Bhattacharyya coe cient. Various test sequences showed the superior tracking performance, obtained with low computational complexity. The paper is organized as follows. Section 2 presents and extends the mean shift property. Section 3 introduces the metric derived from the Bhattacharyya coefcient. The tracking algorithm is developed and analyzed in Section 4. Experiments and comparisons are given in Section 5, and the discussions are in Section 6. h The minimization of the average global error between the estimate and the true density yields the multivariate Epanechnikov kernel 25, p.139] 1 ;1 2 2 KE (x) = 0 cd (d + 2)(1 ; kxk ) if kxk < 1 otherwise (2) where cd is the volume of the unit d-dimensional sphere. Another commonly used kernel is the multivariate normal KN (x) = (2 );d=2 exp ; 1 kxk2 : (3) 2 Let us introduce the pro le of a kernel K as a function k : 0 1) ! R such that K (x) = k(kxk2 ). For example, according to (2) the Epanechnikov pro le is 1 ;1 2 kE (x) = 0 cd (d + 2)(1 ; x) if x < 1 otherwise (4) and from (3) the normal pro le is given by 1 kN (x) = (2 );d=2 exp ; 2 x : (5) Employing the pro le notation we can write the density estimate (1) as n 2! ^K (x) = 1 d X k x ; xi f : (6) nh h i=1 We denote g(x) = ;k0 (x) (7) assuming that the derivative of k exists for all x 2 0 1), except for a nite set of points. A...
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