Crowd collectiveness measure via graph-based node clique learning
This work addresses the challenge of quantifying crowd collectiveness for applications in surveillance and behavior analysis, but appears incremental as it builds on existing graph-based approaches.
The paper tackles the problem of measuring collectiveness in crowd systems by proposing a graph-based node clique learning method, which defines motion coherence between nodes through clique comparisons and tests it on self-driven particle models and a crowd motion database.
Collectiveness motions of crowd systems have attracted a great deal of attentions in recently years. In this paper, we try to measure the collectiveness of a crowd system by the proposed node clique learning method. The proposed method is a graph based method, and investigates the influence from one node to other nodes. A node is represented by a set of nodes which named a clique, which is obtained by spreading information from this node to other nodes in graph. Then only nodes with sufficient information are selected as the clique of this node. The motion coherence between two nodes is defined by node cliques comparing. The collectiveness of a node and the collectiveness of the crowd system are defined by the nodes coherence. Self-driven particle (SDP) model and the crowd motion database are used to test the ability of the proposed method in measuring collectiveness.