IEEE Trans Cybern. 2022 Dec;52(12):12978-12988. doi: 10.1109/TCYB.2021.3095357. Epub 2022 Nov 18.
This article presents a structure constraint matrix factorization framework for different behavior segmentation of the human behavior sequential data. This framework is based on the structural information of the behavior continuity and the high similarity between neighboring frames. Due to the high similarity and high dimensionality of human behavior data, the high-precision segmentation of human behavior is hard to achieve from the perspective of application and academia. By making the behavior continuity hypothesis, first, the effective constraint regular terms are constructed. Subsequently, the clustering framework based on constrained non-negative matrix factorization is established. Finally, the segmentation result can be obtained by using the spectral clustering and graph segmentation algorithm. For illustration, the proposed framework is applied to the Weiz dataset, Keck dataset, mo_86 dataset, and mo_86_9 dataset. Empirical experiments on several public human behavior datasets demonstrate that the structure constraint matrix factorization framework can automatically segment human behavior sequences. Compared to the classical algorithm, the proposed framework can ensure consistent segmentation of sequential points within behavior actions and provide better performance in accuracy.
本文提出了一种基于结构约束矩阵分解的框架，用于对人体行为序列数据进行不同行为的分割。该框架基于行为连续性的结构信息和相邻帧之间的高度相似性。由于人体行为数据具有高度相似性和高维度，因此从应用和学术角度来看，很难实现高精度的人体行为分割。通过进行行为连续性假设，首先构建有效的约束正则项。然后，建立基于约束非负矩阵分解的聚类框架。最后，通过使用谱聚类和图分割算法得到分割结果。为了说明问题，将所提出的框架应用于 Weiz 数据集、Keck 数据集、mo_86 数据集和 mo_86_9 数据集。在几个公开的人体行为数据集上的实验表明，结构约束矩阵分解框架可以自动分割人体行为序列。与经典算法相比，所提出的框架可以保证行为动作内的序列点的一致性分割，并在准确性方面提供更好的性能。
