用参数化时空模型解释光流事件

Michael J. Black
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引用次数: 68

摘要

在传统参数化运动模型(如仿射)的基础上,提出了复杂光流事件的时空表征方法。这些生成的时空模型可能是非线性的或随机的,并且是特定于事件的,因为它们表征特定类型的物体运动(例如坐或走)。在贝叶斯框架中,我们寻求适当的模型、相位、速率、空间位置和尺度来解释图像的变化。该参数空间的后验分布以图像测量为条件,通常是非高斯分布。该分布使用因子采样表示,并使用冷凝算法随时间预测和更新。生成的框架会自动检测、定位和识别运动事件。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Explaining optical flow events with parameterized spatio-temporal models
A spatio-temporal representation for complex optical flow events is developed that generalizes traditional parameterized motion models (e.g. affine). These generative spatio-temporal models may be non-linear or stochastic and are event-specific in that they characterize a particular type of object motion (e.g. sitting or walking). Within a Bayesian framework we seek the appropriate model, phase, rate, spatial position, and scale to account for the image variation. The posterior distribution over this parameter space conditioned on image measurements is typically non-Gaussian. The distribution is represented using factored sampling and is predicted and updated over time using the condensation algorithm. The resulting framework automatically detects, localizes, and recognizes motion events.
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