IEEE Trans Cybern. 2014 Sep;44(9):1646-60. doi: 10.1109/TCYB.2013.2291497.
This paper presents generalized Laplacian eigenmaps, a novel dimensionality reduction approach designed to address stylistic variations in time series. It generates compact and coherent continuous spaces whose geometry is data-driven. This paper also introduces graph-based particle filter, a novel methodology conceived for efficient tracking in low dimensional space derived from a spectral dimensionality reduction method. Its strengths are a propagation scheme, which facilitates the prediction in time and style, and a noise model coherent with the manifold, which prevents divergence, and increases robustness. Experiments show that a combination of both techniques achieves state-of-the-art performance for human pose tracking in underconstrained scenarios.
本文提出了广义拉普拉斯特征映射，这是一种新的降维方法，旨在解决时间序列中的风格变化问题。它生成紧凑且连贯的连续空间，其几何形状是数据驱动的。本文还介绍了基于图的粒子滤波器，这是一种新的方法，用于在从谱维数约减方法得出的低维空间中进行有效跟踪。它的优点是传播方案，它便于时间和风格的预测，以及与流形一致的噪声模型，它可以防止发散，提高鲁棒性。实验表明，这两种技术的结合可以在欠约束场景中实现人体姿态跟踪的最新性能。
