Crowd collectiveness measure via graph-based node clique learning

TL;DR

This paper introduces a graph-based node clique learning method to quantify crowd collectiveness by analyzing influence and motion coherence among nodes, validated on a crowd motion database.

Contribution

It proposes a novel node clique learning approach to measure crowd collectiveness, capturing influence and coherence in a graph-based framework.

Findings

Effective in measuring crowd collectiveness

Validated using SDP model and crowd motion database

Captures influence dynamics among crowd members

Abstract

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.