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Recognizing group activities is challenging due to the difficulties inisolating individual entities, finding the respective roles played by theindividuals and representing the complex interactions among the participants.Individual actions and group activities in videos can be represented in acommon framework as they share the following common feature: both are composedof a set of low-level features describing motions, e.g., optical flow for eachpixel or a trajectory for each feature point, according to a set of compositionconstraints in both temporal and spatial dimensions. In this paper, we presenta unified model to assess the similarity between two given individual or groupactivities. Our approach avoids explicit extraction of individual actors,identifying and representing the inter-person interactions. With the proposedapproach, retrieval from a video database can be performed throughQuery-by-Example; and activities can be recognized by querying videoscontaining known activities. The suggested video matching process can beperformed in an unsupervised manner. We demonstrate the performance of ourapproach by recognizing a set of human actions and football plays.

A Unified Approach for Modeling and Recognition of Individual Actions and Group Activities