Discovering Spatio-Temporal Relationships Among Activities in Videos Using a Relational Topic-Transition Model

Discovering motion activities in videos is a key problem in computer vision, with applications in scene analysis, video categorization, and video indexing. In this paper, we propose a method that uses probabilistic topic modeling for discovering patterns of motion that occur in a given activity. Our method also identifies how the discovered patterns of motion relate to one another in space and time. The topic-modeling approach used by our method is the relational topic model. Our experiments show that our method is able to discover relevant spatio-temporal motion patterns in videos.