Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/29558
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Type: Conference paper
Title: Fast global kernel density mode seeking with application to localisation and tracking
Author: Shen, C.
Brooks, M.
Van Den Hengel, A.
Citation: Tenth IEEE International Conference on Computer Vision (ICCV'05), 2005; 2:1516-1523
Publisher: IEEE
Publisher Place: Los Alamitos, California
Issue Date: 2005
Series/Report no.: IEEE International Conference on Computer Vision
ISBN: 076952334X
9780769523347
ISSN: 1550-5499
Conference Name: IEEE International Conference on Computer Vision (10th : 2005 : Beijing, China)
Statement of
Responsibility: 
Chunhua Shen, Michael J. Brooks and Anton van den Hengel
Abstract: We address the problem of seeking the global mode of a density function using the mean shift algorithm. Mean shift, like other gradient ascent optimisation methods, is susceptible to local maxima, and hence often fails to find the desired global maximum. In this work, we propose a multi-bandwidth mean shift procedure that alleviates this problem, which we term annealed mean shift, as it shares similarities with the annealed importance sampling procedure. The bandwidth of the algorithm plays the same role as the temperature in annealing. We observe that the over-smoothed density function with a sufficiently large bandwidth is uni-modal. Using a continuation principle, the influence of the global peak in the density function is introduced gradually. In this way the global maximum is more reliably located. Generally, the price of this annealing-like procedure is that more iterations are required. Since it is imperative that the computation complexity is minimal in real-time applications such as visual tracking. We propose an accelerated version of the mean shift algorithm. Compared with the conventional mean shift algorithm, the accelerated mean shift can significantly decrease the number of iterations required for convergence. The proposed algorithm is applied to the problems of visual tracking and object localisation. We empirically show on various data sets that the proposed algorithm can reliably find the true object location when the starting position of mean shift is far away from the global maximum, in contrast with the conventional mean shift algorithm that will usually get trapped in a spurious local maximum.
Description: Copyright © 2005 IEEE.
RMID: 0020052122
DOI: 10.1109/ICCV.2005.94
Appears in Collections:Computer Science publications

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