Xu Xinyu, Li Baoxin
Department of Computer Science and Engineering, Arizona State University, Tempe, AZ 85287, USA.
IEEE Trans Image Process. 2007 Mar;16(3):838-49. doi: 10.1109/tip.2007.891074.
Particle filters can become quite inefficient when being applied to a high-dimensional state space since a prohibitively large number of samples may be required to approximate the underlying density functions with desired accuracy. In this paper, by proposing an adaptive Rao-Blackwellized particle filter for tracking in surveillance, we show how to exploit the analytical relationship among state variables to improve the efficiency and accuracy of a regular particle filter. Essentially, the distributions of the linear variables are updated analytically using a Kalman filter which is associated with each particle in a particle filtering framework. Experiments and detailed performance analysis using both simulated data and real video sequences reveal that the proposed method results in more accurate tracking than a regular particle filter.
当应用于高维状态空间时，粒子滤波器可能会变得效率极低，因为可能需要数量多到令人望而却步的样本，才能以所需的精度逼近潜在的密度函数。在本文中，我们提出了一种用于监控跟踪的自适应 Rao-Blackwellized 粒子滤波器，展示了如何利用状态变量之间的解析关系来提高常规粒子滤波器的效率和精度。本质上，线性变量的分布是使用卡尔曼滤波器进行解析更新的，该卡尔曼滤波器与粒子滤波框架中的每个粒子相关联。使用模拟数据和真实视频序列进行的实验及详细性能分析表明，所提出的方法比常规粒子滤波器能实现更精确的跟踪。
