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Object Tracking Using Probabilistic Principal Component Analysis Based on Particle Filtering Framework

机译：基于粒子滤波框架的概率主成分分析目标跟踪

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In this paper, an object tracking approach is introduced for color video sequences.The approach presents the integration of color distributions and probabilistic principal component analysis (PPCA) into particle filtering framework.Color distributions are robust to partial occlusion, are rotation and scale invariant and are calculated efficiently.Principal Component Analysis (PCA) is used to update the eigenbasis and the mean, which can reflect the appearance changes of the tracked object. And a low dimensional subspace representation of PPCA efficiently adapts to these changes of appearance of the target object.At the same time, a forgetting factor is incorporated into the updating process, which can be used to economize on processing time and enhance the efficiency of object tracking.Computer simulation experiments demonstrate the effectiveness and the robustness of the proposed tracking algorithm when the target object undergoes pose and scale changes, defilade and complex background.
机译：本文介绍了一种针对彩色视频序列的对象跟踪方法，该方法将颜色分布和概率主成分分析（PPCA）集成到粒子滤波框架中，颜色分布对于部分遮挡具有鲁棒性，旋转和比例不变且主成分分析（PCA）用于更新特征本底和均值，以反映被跟踪对象的外观变化。 PPCA的低维子空间表示有效地适应了目标对象外观的这些变化。同时，在更新过程中加入了遗忘因素，可以节省处理时间并提高对象效率计算机仿真实验证明了该跟踪算法在目标物体发生姿态和比例变化，偏移和复杂背景时的有效性和鲁棒性。

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