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Unformatted text preview: IEEE CVPR 2000 RealTime Tracking of NonRigid Objects using Mean Shift Dorin Comaniciu Visvanathan Ramesh Peter Meer Imaging & Visualization Department
Siemens Corporate Research
755 College Road East, Princeton, NJ 08540 Electrical & Computer Engineering Department
Rutgers University
94 Brett Road, Piscataway, NJ 08855 Abstract 2 Mean Shift Analysis A new method for realtime tracking of nonrigid objects seen from a moving camera is proposed. The central computational module is based on the mean shift
iterations and nds the most probable target position in
the current frame. The dissimilarity between the target
model (its color distribution) and the target candidates
is expressed by a metric derived from the Bhattacharyya
coe cient. The theoretical analysis of the approach
shows that it relates to the Bayesian framework while
providing a practical, fast and e cient solution. The
capability of the tracker to handle in realtime partial
occlusions, signi cant clutter, and target scale variations, is demonstrated for several image sequences. We de ne next the sample mean shift, introduce the
iterative mean shift procedure, and present a new theorem showing the convergence for kernels with convex
and monotonic pro les. For applications of the mean
shift property in low level vision ( ltering, segmentation) see 6]. 2.1 Sample Mean Shift Given a set fxi gi=1:::n of n points in the ddimensional space Rd, the multivariate kernel density
estimate with kernel K (x) and window radius (bandwidth) h, computed in the point x is given by
n
X
f^(x) = 1 d K x ; xi :
(1) 1 Introduction nh i=1 The e cient tracking of visual features in complex
environments is a challenging task for the vision community. Realtime applications such as surveillance and
monitoring 10], perceptual user interfaces 4], smart
rooms 16, 28], and video compression 12] all require
the ability to track moving objects. The computational
complexity of the tracker is critical for most applications, only a small percentage of a system resources being allocated for tracking, while the rest is assigned to
preprocessing stages or to highlevel tasks such as recognition, trajectory interpretation, and reasoning 24].
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 Fall '10
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 Math, The Land

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