Nonparametric background generation
Journal of Visual Communication and Image Representation
Robust tracking with motion estimation and local Kernel-based color modeling
Image and Vision Computing
MAP ZDF segmentation and tracking using active stereo vision: Hand tracking case study
Computer Vision and Image Understanding
Adaptive pyramid mean shift for global real-time visual tracking
Image and Vision Computing
Color constancy via convex kernel optimization
ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part I
Multibandwidth kernel-based object tracking
Advances in Artificial Intelligence - Special issue on machine learning paradigms for modeling spatial and temporal information in multimedia data mining
Target Tracking Using Multiple Patches and Weighted Vector Median Filters
Journal of Mathematical Imaging and Vision
A novel particle filter with implicit dynamic model for irregular motion tracking
Machine Vision and Applications
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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.