Description

The system present a coherent, discriminative framework for simultaneously tracking multiple people and estimating their collective activities. Instead of treating the two problems separately, our model is grounded in the intuition that a strong correlation exists between a person’s motion, their activity, and the motion and activities of other nearby people. Instead of directly linking the solutions to these two problems, we introduce a hierarchy of activity types that creates a natural progression that leads from a specific person’s motion to the activity of the group as a whole. The system propose a two-level hierarchical graphical model, which learns the relationship between tracks, relationship between tracks, and their corresponding activity segments, as well as the spatiotemporal relationships across activity segments. We also propose an algorithm for solving this otherwise intractable joint inference problem by combining belief propagation with a version of the branch and bound algorithm equipped with integer programming. 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.