Spatio-temporal interaction model for crowd video analysis
This work addresses crowd video analysis for surveillance and safety applications, presenting an incremental improvement with novel methods for known bottlenecks.
The authors tackled the problem of analyzing crowd behavior at multiple granularities by proposing an unsupervised spatio-temporal interaction model and group detection algorithm, achieving superlative performance over state-of-the-art methods in extensive experiments.
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.