A Novel Voronoi-based Convolutional Neural Network Framework for Pushing Person Detection in Crowd Videos

TL;DR

This paper introduces a Voronoi-based convolutional neural network framework for automatically detecting pushing behavior in crowd videos, enhancing microscopic analysis of crowd interactions.

Contribution

The paper presents a novel Voronoi-based feature extraction method combined with CNNs for microscopic pushing detection in crowd videos, outperforming existing baseline methods.

Findings

Framework outperforms seven baseline methods

Effective microscopic pushing detection in crowd videos

New dataset created for evaluation

Abstract

Analyzing the microscopic dynamics of pushing behavior within crowds can offer valuable insights into crowd patterns and interactions. By identifying instances of pushing in crowd videos, a deeper understanding of when, where, and why such behavior occurs can be achieved. This knowledge is crucial to creating more effective crowd management strategies, optimizing crowd flow, and enhancing overall crowd experiences. However, manually identifying pushing behavior at the microscopic level is challenging, and the existing automatic approaches cannot detect such microscopic behavior. Thus, this article introduces a novel automatic framework for identifying pushing in videos of crowds on a microscopic level. The framework comprises two main components: i) Feature extraction and ii) Video labeling. In the feature extraction component, a new Voronoi-based method is developed for determining the local regions associated with each person in the input video. Subsequently, these regions are fed into EfficientNetV1B0 Convolutional Neural Network to extract the deep features of each person over time. In the second component, a combination of a fully connected layer with a Sigmoid activation function is employed to analyze these deep features and annotate the individuals involved in pushing within the video. The framework is trained and evaluated on a new dataset created using six real-world experiments, including their corresponding ground truths. The experimental findings indicate that the suggested framework outperforms seven baseline methods that are employed for comparative analysis purposes.