Han Bohyung, Zhu Ying, Comaniciu Dorin, Davis Larry S
Advanced Project Center, Mobileye Vision Technologies, 12 Venderventer Ave., Princeton, NJ 08542, USA.
IEEE Trans Pattern Anal Mach Intell. 2009 May;31(5):919-30. doi: 10.1109/TPAMI.2008.134.
Particle filtering is frequently used for visual tracking problems since it provides a general framework for estimating and propagating probability density functions for nonlinear and non-Gaussian dynamic systems. However, this algorithm is based on a Monte Carlo approach and the cost of sampling and measurement is a problematic issue, especially for high-dimensional problems. We describe an alternative to the classical particle filter in which the underlying density function has an analytic representation for better approximation and effective propagation. The techniques of density interpolation and density approximation are introduced to represent the likelihood and the posterior densities with Gaussian mixtures, where all relevant parameters are automatically determined. The proposed analytic approach is shown to perform more efficiently in sampling in high-dimensional space. We apply the algorithm to real-time tracking problems and demonstrate its performance on real video sequences as well as synthetic examples.
粒子滤波常用于视觉跟踪问题，因为它为估计和传播非线性非高斯动态系统的概率密度函数提供了一个通用框架。然而，该算法基于蒙特卡罗方法，采样和测量成本是一个有问题的问题，特别是对于高维问题。我们描述了一种经典粒子滤波器的替代方法，其中基础密度函数具有解析表示，以实现更好的近似和有效传播。引入密度插值和密度近似技术，用高斯混合来表示似然和后验密度，其中所有相关参数都是自动确定的。所提出的解析方法在高维空间采样中表现出更高的效率。我们将该算法应用于实时跟踪问题，并在真实视频序列以及合成示例上展示其性能。
