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首页> 外文期刊>International Journal of Computer Vision >Simultaneous motion detection and background reconstruction with a conditional mixed-state markov random field
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Simultaneous motion detection and background reconstruction with a conditional mixed-state markov random field

机译：条件混合状态马尔可夫随机场的同时运动检测和背景重建

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摘要

In this work we present a new way of simultaneously solving the problems of motion detection and background image reconstruction. An accurate estimation of the background is only possible if we locate the moving objects. Meanwhile, a correct motion detection is achieved if we have a good available background model. The key of our joint approach is to define a single random process that can take two types of values, instead of defining two different processes, one symbolic (motion detection) and one numeric (background intensity estimation). It thus allows to exploit the (spatio-temporal) interaction between a decision (motion detection) and an estimation (intensity reconstruction) problem. Consequently, the meaning of solving both tasks jointly, is to obtain a single optimal estimate of such a process. The intrinsic interaction and simultaneity between both problems is shown to be better modeled within the so-called mixed-state statistical framework, which is extended here to account for symbolic states and conditional random fields. Experiments on real sequences and comparisons with existing motion detection methods support our proposal. Further implications for video sequence inpainting will be also discussed.
机译：在这项工作中，我们提出了一种同时解决运动检测和背景图像重建问题的新方法。仅当我们定位运动对象时，才能对背景进行准确估计。同时，如果我们有良好的可用背景模型，则可以实现正确的运动检测。我们联合方法的关键是定义一个可以采用两种类型值的随机过程，而不是定义两种不同过程，一种是符号（运动检测），另一种是数字（背景强度估计）。因此，它允许利用决策（运动检测）和估计（强度重建）问题之间的（时空）交互。因此，共同解决这两个任务的意义在于获得该过程的单个最佳估计。这两个问题之间的内在相互作用和同时性在所谓的混合状态统计框架中得到了更好的建模，在此将其扩展为考虑符号状态和条件随机字段。对真实序列进行的实验以及与现有运动检测方法的比较支持了我们的建议。还将讨论视频序列修复的其他含义。

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