Hierarchical Union-of-Subspaces Model for Human Activity Summarization
Hierarchical Union-of-Subspaces Model for Human Activity Summarization
复制标题
DOI：
10.1109/iccvw.2015.138
复制
发表时间：
2015-12
期刊：
影响因子：
--
通讯作者：
Tong Wu;Prudhvi K. Gurram;R. Rao;W. Bajwa
中科院分区：
文献类型：
--
作者：
Tong Wu;Prudhvi K. Gurram;R. Rao;W. Bajwa
提出了一种分层子空间联合模型，用于在大规模视频数据流中进行半监督人体活动摘要。低维子空间的联合模型用于从人类活动的高维视频序列集合中学习有意义的动作属性。开发了一种称为分层稀疏子空间聚类（HSSC）的方法，通过捕获不同子空间中每个动作的变化或移动，以无监督的方式从数据中学习该模型，这使得人类动作可以表示为从一个子空间到另一个子空间的过渡序列。这些过渡序列可以用于人类动作识别。动作属性也可以使用分层结构的不同级别的子空间以多个分辨率表示。通过可视化和标记这些动作属性，层次模型可以用于语义总结长视频序列的人类行动在不同的尺度。该模型的有效性是通过实验证明在三个真实世界的人类动作数据集的动作识别和语义概括的行动，使用不同的分辨率的行动属性。
A hierarchical union-of-subspaces model is proposed for performing semi-supervised human activity summarization in large streams of video data. The union of low-dimensional subspaces model is used to learn meaningful action attributes from a collection of high-dimensional video sequences of human activities. An approach called hierarchical sparse subspace clustering (HSSC) is developed to learn this model from the data in an unsupervised manner by capturing the variations or movements of each action in different subspaces, which allow the human actions to be represented as sequences of transitions from one subspace to another. These transition sequences can be used for human action recognition. The action attributes can also be represented at multiple resolutions using the subspaces at different levels of the hierarchical structure. By visualizing and labeling these action attributes, the hierarchical model can be used to semantically summarize long video sequences of human actions at different scales. The effectiveness of the proposed model is demonstrated through experiments on three real-world human action datasets for action recognition and semantic summarization of the actions using different resolutions of the action attributes.