Crowd Characterization in Surveillance Videos Using Deep-Graph Convolutional Neural Network.

Abstract

Crowd behavior is a natural phenomenon that can provide valuable insight into the crowd characterization process. Modeling the visual appearance of a large crowd gathering can reveal meaningful information about its dynamics. Parametric modeling can be used to develop efficient and robust crowd monitoring systems. A crowd can be structured or unstructured based on the organization. In this article, crowd characterization has been mapped to a graph classification problem to classify movements based on order parameter ( ϕ ), active force components, and steadiness (Reynolds number). The graphs are constructed from the motion groups obtained using an active Langevin framework. These graphs are processed using a deep graph convolutional neural network for crowd characterization. For experimentation, we have prepared a dataset comprising of videos from popular publicly available datasets and our own recorded videos. The proposed framework has been compared with the latest deep learning-based frameworks in terms of accuracy and area under the curve (AUC). We have obtained a 4%-5% improvement in accuracy and AUC values over the existing frameworks. The insights obtained from the proposed framework can be used for better crowd monitoring and management.