Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/42018
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Type: Journal article
Title: Fast global kernel density mode seeking: Applications to localization and tracking
Author: Shen, C.
Brooks, M.
Van Den Hengel, A.
Citation: IEEE Transactions on Image Processing, 2007; 16(5):1457-1469
Publisher: IEEE-Inst Electrical Electronics Engineers Inc
Issue Date: 2007
ISSN: 1057-7149
1941-0042
Statement of
Responsibility: 
Chunhua Shen, Michael J. Brooks, Anton van den Hengel
Abstract: Tracking objects in video using the mean shift (MS) technique has been the subject of considerable attention. In this work, we aim to remedy one of its shortcomings. MS, like other gradient ascent optimization methods, is designed to find local modes. In many situations, however, we seek the global mode of a density function. The standard MS tracker assumes that the initialization point falls within the basin of attraction of the desired mode. When tracking objects in video this assumption may not hold, particularly when the target's displacement between successive frames is large. In this case, the local and global modes do not correspond and the tracker is likely to fail. A novel multibandwidth MS procedure is proposed which converges to the global mode of the density function, regardless of the initialization point. We term the procedure annealed MS, as it shares similarities with the annealed importance sampling procedure. The bandwidth of the procedure plays the same role as the temperature in conventional annealing. We observe that an over-smoothed density function with a sufficiently large bandwidth is unimodal. 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. Since it is imperative that the computational complexity is minimal for real-time applications, such as visual tracking, we also propose an accelerated version of the algorithm. This significantly decreases the number of iterations required to achieve convergence. We show on various data sets that the proposed algorithm offers considerable promise in reliably and rapidly finding the true object location when initialized from a distant point
Keywords: Image Interpretation, Computer-Assisted; Image Enhancement; Sensitivity and Specificity; Reproducibility of Results; Algorithms; Motion; Artificial Intelligence; Video Recording; Pattern Recognition, Automated
Description: Copyright © 2007 IEEE
RMID: 0020070649
DOI: 10.1109/TIP.2007.894233
Appears in Collections:Computer Science publications

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