In this paper, we propose a framework for recognizing human activities that uses only in-topic dominant codewords and a mixture of intertopic vectors. Latent Dirichlet allocation (LDA) is used to develop approximations of human motion primitives; these are mid-level representations, and they adaptively integrate dominant vectors when classifying human activities. In LDA topic modeling, action videos (documents) are represented by a bag-of-words (input from a dictionary), and these are based on improved dense trajectories. The output topics correspond to human motion primitives, such as finger moving or subtle leg motion. We eliminate the impurities, such as missed tracking or changing light conditions, in each motion primitive. The assembled vector of motion primitives is an improved representation of the action. We demonstrate our method on four different datasets.

本文提出了一种识别人类活动的框架，该框架只使用主题内主导码词和混合的跨主题向量。采用潜在狄利克雷分配法（LDA）来开发人类运动原语的近似表示，这些原语是中级表示，当分类人类活动时，它们会自适应地整合主导向量。在LDA主题建模中，将行动视频（文档）表示为词袋（从字典输入），并基于改进的密集轨迹。输出主题对应于人类运动原语，例如手指移动或微妙的腿部动作。 我们消除了每个运动原语中的杂质，例如漏跟踪或光线变化。运动原语的组装向量是动作的改进表示。我们在四个不同的数据集上展示了该方法。

基于主题模型的行为识别中主导编码词选择