Markov-based failure prediction for human motion analysis

S. Dockstader, Nikita S. Imennov, A. Tekalp
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引用次数: 6

Abstract

This paper presents a new method of detecting and predicting motion tracking failures with applications in human motion and gait analysis. We define a tracking failure as an event and describe its temporal characteristics using a hidden Markov model (HMM). This stochastic model is trained using previous examples of tracking failures. We derive vector observations for the HMM using the noise covariance matrices characterizing a tracked, 3D structural model of the human body. We show a causal relationship between the conditional output probability of the HMM, as transformed using a logarithmic mapping function, and impending tracking failures. Results are illustrated on several multi-view sequences of complex human motion.
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基于马尔可夫的人体运动分析失效预测
本文提出了一种检测和预测运动跟踪故障的新方法,并在人体运动和步态分析中应用。我们将跟踪故障定义为事件,并使用隐马尔可夫模型(HMM)描述其时间特征。这个随机模型是用之前跟踪失败的例子来训练的。我们使用表征跟踪的人体三维结构模型的噪声协方差矩阵来推导HMM的矢量观测值。我们展示了HMM的条件输出概率(使用对数映射函数转换)与即将发生的跟踪故障之间的因果关系。结果说明了几个多视图序列的复杂人体运动。
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