Probabilistic topic modelings, such as latent Dirichlet allocation (LDA) and correlated topic models (CTM), have recently emerged as powerful statistical tools for processing video content. They share an important property, i.e., using a common set of topics to model all data. However, such property can be too restrictive for modeling complex visual data such as crowd scenes where multiple fields of heterogeneous data jointly provide rich information about objects and events. This paper proposes graph-based extensions of LDA and CTM, referred to as GLDA and GCTM, to learn and analyze motion patterns by trajectory clustering in a highly cluttered and crowded environment. Unlike previous works that relied on a scene prior, we apply a spatio-temporal graph to uncover the spatial and temporal coherence between the trajectories of crowd motion during the learning process. The presented models advance the conventional approaches by integrating a manifold-based clustering as initialization and iterative statistical inference as optimization. The output of GLDA and GCTM are mid-level features that represent the motion patterns used later to generate trajectory clusters. Experiments on three different datasets show the effectiveness of the approaches in trajectory clustering and crowd motion modeling.

中文翻译：

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基于图的主题模型用于人群视频中的轨迹聚类

诸如潜在的狄利克雷分配（LDA）和相关主题模型（CTM）之类的概率主题建模最近已成为处理视频内容的强大统计工具。它们具有重要的属性，即使用一组通用的主题来对所有数据进行建模。但是，这种属性对于建模复杂的视觉数据（例如人群场景）可能过于局限，在人群场景中，异构数据的多个字段共同提供了有关对象和事件的丰富信息。本文提出了基于图形的LDA和CTM扩展，称为GLDA和GCTM，以在高度混乱和拥挤的环境中通过轨迹聚类学习和分析运动模式。不像以前的作品依赖一个场景，我们应用时空图来揭示学习过程中人群运动轨迹之间的时空连贯性。提出的模型通过集成基于流形的聚类作为初始化和迭代统计推断作为优化来推进传统方法。GLDA和GCTM的输出是中级功能，代表以后用于生成轨迹簇的运动模式。在三个不同的数据集上进行的实验表明，该方法在轨迹聚类和人群运动建模中是有效的。GLDA和GCTM的输出是中级功能，代表以后用于生成轨迹簇的运动模式。在三个不同的数据集上进行的实验表明，该方法在轨迹聚类和人群运动建模中是有效的。GLDA和GCTM的输出是中级功能，代表以后用于生成轨迹簇的运动模式。在三个不同的数据集上进行的实验表明，该方法在轨迹聚类和人群运动建模中是有效的。