Fast Global Kernel Density Mode Seeking with Application to Localisation and Tracking

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 applicatio...

• Type: Conference
• Year: 2005
• Where: ICCV
• Authors: Chunhua Shen, Michael J. Brooks, Anton van den Hengel
• Keywords: Computer Vision, Density Function, ICCV 2005, Mean Shift, Mean Shift Algorithm