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Adaptive Rao–Blackwellized Particle Filter and Its Evaluation for Tracking in Surveillance

机译:自适应Rao-Blackwellized粒子滤波器及其在监视跟踪中的评估

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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
机译:当将粒子过滤器应用于高维状态空间时,其效率可能会非常低下,因为可能需要大量样本才能以所需的精度近似基本密度函数。在本文中,通过提出一种自适应Rao-Blackwellized粒子过滤器进行监视跟踪,我们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过滤器的效率和准确性。本质上,使用与粒子过滤框架中的每个粒子相关联的卡尔曼滤波器来分析更新线性变量的分布。使用模拟数据和真实视频序列进行的实验和详细的性能分析表明,与常规粒子滤波器相比,该方法可实现更精确的跟踪

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