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Tracking of feature points in a scene of moving rigid objects by Bayesian switching structure model with particle filter

机译：带粒子滤波的贝叶斯切换结构模型在运动刚性物体场景中的特征点跟踪

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摘要

Causal estimation of multiple feature points trajectories by using a switching state space model is proposed. The state vector of the model consists of the position of each feature point, the velocity of each rigid object, and some indicator variables for each feature point. Ther are two types of indicator variables: an object indicator representing the association between the feature point and rigid object, and an aperture indicator representing the attribute of the point, e.g. aperture or not. By estimating the state vector using a Rao-Blackwellized particle filter, smooth trajectories of feature points, velocity of objects, object indicators, and aperture indicators are obtained simultaneously. Performance on a real image sequence is presented by comparing to a Kalman filter being given true indicators.
机译：提出了基于状态切换模型的多特征点轨迹的因果估计。模型的状态向量由每个特征点的位置，每个刚性物体的速度以及每个特征点的一些指标变量组成。这是两种类型的指示符变量：代表特征点和刚体之间的关联的对象指示符，以及代表点属性的光圈指示符，例如：光圈与否。通过使用Rao-Blackwellized粒子滤波器估计状态向量，可以同时获得特征点的平滑轨迹，物体的速度，物体指示器和光圈指示器。通过与给定真实指示符的卡尔曼滤波器进行比较，可以显示真实图像序列的性能。

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