Graph-based topic models for trajectory clustering in crowd videos

Abstract

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 graphbased 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 (STG) 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.

Trajectory dimensionality reduction and hyperparameter settings of DBSCAN for trajectory clustering

“…Ref. [40] proposed graph‐based extensions of LDA and correlated topic models (CTM) to learn and analyse motion patterns by trajectory clustering in a highly cluttered and crowded environment. The two models replaced the corpus, document, topic and words in the conventional models with pathway, trajectory, motion pattern and visual codes in the graph‐based models.…”

An Approach to Spatiotemporal Trajectory Clustering Based on Community Detection

“…More recently, there is an increasing interest on time series clustering using graphs [14,15]. Traditional analysis methods only focus on the local relationship between data samples, while ignoring the global information.…”