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首页> 外文会议>European Conference on Computer Vision(ECCV 2004) pt.4; 20040511-20040514; Prague; CZ >Stereo Based 3D Tracking and Scene Learning, Employing Particle Filtering within EM
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Stereo Based 3D Tracking and Scene Learning, Employing Particle Filtering within EM

机译：基于立体的3D跟踪和场景学习，在EM中使用粒子滤波

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摘要

We present a generative probabilistic model for 3D scenes with stereo views. With this model, we track an object in 3 dimensions while simultaneously learning its appearance and the appearance of the background. By using a generative model for the scene, we are able to aggregate evidence over time. In addition, the probabilistic model naturally handles sources of variability. For inference and learning in the model, we formulate an Expectation Maximization (EM) algorithm where Rao-Blackwellized Particle filtering is used in the E step. The use of stereo views of the scene is a strong source of disambiguating evidence and allows rapid convergence of the algorithm. The update equations have an appealing form and as a side result, we give a generative probabilistic interpretation for the Sum of Squared Differences (SSD) metric known from the field of Stereo Vision.
机译：我们提出了具有立体视图的3D场景的生成概率模型。使用此模型，我们可以在3个维度上跟踪对象，同时了解其外观和背景外观。通过使用场景的生成模型，我们可以随着时间的推移收集证据。此外，概率模型自然可以处理可变性的来源。为了在模型中进行推理和学习，我们制定了期望最大化（EM）算法，其中在E步骤中使用了Rao-Blackwellized粒子滤波。使用场景的立体视图是消除歧义证据的重要来源，并且可以使算法快速收敛。更新方程具有吸引人的形式，并且附带的结果是，我们对立体视觉领域中已知的平方差总和（SSD）度量给出了生成概率的解释。

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