深度传感器网络联合概率人检测的时间平滑

J. Wetzel, Astrid Laubenheimer, M. Heizmann
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引用次数: 0

摘要

深度传感器网络中的广域室内人员检测是许多应用的基础,例如人员计数或客户行为分析。现有的概率方法使用近似随机推理来估计场景中单个时间步中人的边际概率分布。在这项工作中,我们研究了如何利用多视图深度观测时间序列给出的时间背景来正则化平均场变分推理优化过程。我们提出了一个基于概率网格的动态模型,并推导了相应的平均场更新规则,以有效地近似场景中出现的人在空间和时间上的联合概率分布。我们的实验表明,提出的时间正则化导致对期望概率分布的更鲁棒估计,并提高了检测性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Temporal Smoothing for Joint Probabilistic People Detection in a Depth Sensor Network
Wide-area indoor people detection in a network of depth sensors is the basis for many applications, e.g. people counting or customer behavior analysis. Existing probabilistic methods use approximative stochastic inference to estimate the marginal probability distribution of people present in the scene for a single time step. In this work we investigate how the temporal context, given by a time series of multi-view depth observations, can be exploited to regularize a mean-field variational inference optimization process. We present a probabilistic grid based dynamic model and deduce the corresponding mean-field update regulations to effectively approximate the joint probability distribution of people present in the scene across space and time. Our experiments show that the proposed temporal regularization leads to a more robust estimation of the desired probability distribution and increases the detection performance.
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