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Fast Global Kernel Density Mode Seeking: Applications to Localization and Tracking

机译:快速全球内核密度模式寻求:在本地化和跟踪中的应用

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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.
机译:使用均值漂移(MS)技术跟踪视频中的对象已成为相当关注的主题。在这项工作中,我们旨在弥补其缺点之一。与其他梯度上升优化方法一样,MS旨在查找局部模式。但是,在许多情况下,我们寻求密度函数的全局模式。标准MS跟踪器假定初始化点位于所需模式的吸引力范围内。当跟踪视频中的对象时,此假设可能不成立,尤其是当目标在连续帧之间的位移较大时。在这种情况下,本地和全局模式不对应,并且跟踪器可能会失败。提出了一种新颖的多带宽MS程序,该程序收敛于密度函数的全局模式,而与初始化点无关。我们将其称为退火MS程序,因为它与退火重要性采样程序具有相似之处。该过程的带宽与常规退火中的温度起着相同的作用。我们观察到,具有足够大带宽的过度平滑的密度函数是单峰的。使用延续原理,逐步介绍了全局峰对密度函数的影响。这样,可以更可靠地定位全局最大值。由于对于实时应用(例如视觉跟踪)而言,必须使计算复杂性最小,因此,我们还提出了该算法的加速版本。这大大减少了实现收敛所需的迭代次数。我们在各种数据集上表明,从远处初始化时,所提出的算法在可靠,快速地找到真实对象位置方面提供了可观的前景。

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