Context-Aware Activity Modeling Using Hierarchical Conditional Random Fields
In this paper, rather than modeling activities in videos individually, we jointly model and recognize related activities in a scene using both motion and context features. This is motivated from the observations that activities related in space and time rarely occur independently and can serve as the context for each other. We propose a two-layer conditional random field model, that represents the action segments and activities in a hierarchical manner. The model allows the integration of both motion and various context features at different levels and automatically learns the statistics that capture the patterns of the features. With weakly labeled training data, the learning problem is formulated as a max-margin problem and is solved by an iterative algorithm. Rather than generating activity labels for individual activities, our model simultaneously predicts an optimum structural label for the related activities in the scene. We show promising results on the UCLA Office Dataset and VIRAT Ground Dataset that demonstrate the benefit of hierarchical modeling of related activities using both motion and context features.