Khan Zia, Balch Tucker, Dellaert Frank
GVU Center, College of Computing, Georgia Institute of Technology, Atlanta 30332-0760, USA.
IEEE Trans Pattern Anal Mach Intell. 2005 Nov;27(11):1805-19. doi: 10.1109/TPAMI.2005.223.
We describe a particle filter that effectively deals with interacting targets--targets that are influenced by the proximity and/or behavior of other targets. The particle filter includes a Markov random field (MRF) motion prior that helps maintain the identity of targets throughout an interaction, significantly reducing tracker failures. We show that this MRF prior can be easily implemented by including an additional interaction factor in the importance weights of the particle filter. However, the computational requirements of the resulting multitarget filter render it unusable for large numbers of targets. Consequently, we replace the traditional importance sampling step in the particle filter with a novel Markov chain Monte Carlo (MCMC) sampling step to obtain a more efficient MCMC-based multitarget filter. We also show how to extend this MCMC-based filter to address a variable number of interacting targets. Finally, we present both qualitative and quantitative experimental results, demonstrating that the resulting particle filters deal efficiently and effectively with complicated target interactions.
我们描述了一种粒子滤波器，它能有效地处理相互作用的目标——即受其他目标的接近程度和/或行为影响的目标。该粒子滤波器包括一个马尔可夫随机场（MRF）运动先验，有助于在整个交互过程中保持目标的身份，显著减少跟踪器故障。我们表明，通过在粒子滤波器的重要性权重中包含一个额外的交互因子，可以轻松实现这种MRF先验。然而，所得多目标滤波器的计算要求使其不适用于大量目标。因此，我们用一种新颖的马尔可夫链蒙特卡罗（MCMC）采样步骤取代粒子滤波器中的传统重要性采样步骤，以获得一个更高效的基于MCMC的多目标滤波器。我们还展示了如何扩展这种基于MCMC的滤波器以处理可变数量的相互作用目标。最后，我们给出了定性和定量的实验结果，证明所得的粒子滤波器能有效且高效地处理复杂的目标交互。
