  We present an unsupervised approach to analyze crowd at various levels of
granularity $-$ individual, group and collective. We also propose a motion
model to represent the collective motion of the crowd. The model captures the
spatio-temporal interaction pattern of the crowd from the trajectory data
captured over a time period. Furthermore, we also propose an effective group
detection algorithm that utilizes the eigenvectors of the interaction matrix of
the model. We also show that the eigenvalues of the interaction matrix
characterize various group activities such as being stationary, walking,
splitting and approaching. The algorithm is also extended trivially to
recognize individual activity. Finally, we discover the overall crowd behavior
by classifying a crowd video in one of the eight categories. Since the crowd
behavior is determined by its constituent groups, we demonstrate the usefulness
of group level features during classification. Extensive experimentation on
various datasets demonstrates a superlative performance of our algorithms over
the state-of-the-art methods.
