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