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Robust, on-line, view-based appearance models for visual motion analysis and visual tracking

机译:强大的,基于视图的在线外观模型,用于视觉运动分析和视觉跟踪

摘要

A robust, adaptive, appearance model is disclosed that includes both a stable model component, learned over a long time course, and a transient component, learned over a relatively short time course (e.g., a 2-frame motion component and/or an outlier processing component). An on-line EM-algorithm is used to adapt the appearance model parameters over time. An implementation of this approach is developed for an appearance model based on the filter responses from a steerable pyramid. The appearance model is used in a motion-based tracking algorithm to provide robustness in the face of image outliers, such as those caused by occlusions. It is also provides the ability to adapt to natural changes in appearance, such as those due to facial expressions, or variations in 3D pose.
机译:公开了一种鲁棒的,自适应的外观模型,其包括在较长时间过程中学习的稳定模型分量和在较短时间过程中学习的瞬态分量(例如2帧运动分量和/或离群值处理组件)。在线EM算法用于随时间调整外观模型参数。基于来自可控金字塔的滤波器响应,为外观模型开发了此方法的实现。外观模型用于基于运动的跟踪算法中,可提供面对图像异常值(例如由遮挡引起的异常值)的鲁棒性。它还提供了适应外观自然变化的能力,例如由于面部表情或3D姿势变化引起的变化。

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