Crowd motion analysis for group detection

Understanding crowd dynamics is an interesting problem in computer vision owing to its various applications. We propose a dynamical system to model the dynamics of collective motion of the crowd. The model learns the spatio-temporal interaction pattern of the crowd from the track data captured over a time period. The model is trained under a least square formulation with spatial and temporal constraints. The spatial constraint allows the model to consider only the neighbors of a particular agent and the temporal constraint enforces temporal smoothness in the model. We also propose an effective group detection algorithm that utilizes the eigenvectors of the interaction matrix of the model. The group detection is cast as a spectral clustering problem. Extensive experimentation demonstrates a superlative performance of our group detection algorithm over state-of-the-art methods.