A Novel Voronoi-based Convolutional Neural Network Framework for Pushing
  Person Detection in Crowd Videos
Ahmed Alia Mohammed Maree Mohcine Chraibi Armin Seyfried
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.