使用混合蒙特卡罗滤波进行人员跟踪

Kiam Choo, David J. Fleet
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引用次数: 238

摘要

粒子滤波用于非线性动态系统的隐状态估计。考虑到人体的非线性动力学以及状态与图像观测之间的非线性关系,人体三维运动推理是一种自然的应用。然而,粒子滤波器的应用仅限于状态变量数量相对较少的情况,因为高维问题所需的样本数量可能令人望而却步。我们描述了一种使用混合蒙特卡罗(HMC)在高维空间中获取样本的滤波器。它使用多个马尔可夫链,使用后验梯度来快速探索状态空间,从后验中产生公平的样本。我们发现HMC滤波器在28d人跟踪问题上比传统粒子滤波器快几千倍。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
People tracking using hybrid Monte Carlo filtering
Particle filters are used for hidden state estimation with nonlinear dynamical systems. The inference of 3-D human motion is a natural application, given the nonlinear dynamics of the body and the nonlinear relation between states and image observations. However, the application of particle filters has been limited to cases where the number of state variables is relatively small, because the number of samples needed with high dimensional problems can be prohibitive. We describe a filter that uses hybrid Monte Carlo (HMC) to obtain samples in high dimensional spaces. It uses multiple Markov chains that use posterior gradients to rapidly explore the state space, yielding fair samples from the posterior. We find that the HMC filter is several thousand times faster than a conventional particle filter on a 28 D people tracking problem.
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