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.

翻译：分析人群内部推挤行为的微观动力学，可为人群模式及互动提供宝贵见解。通过识别人群视频中的推挤实例，能够更深入地理解此类行为发生的时间、地点及原因。这一认知对于制定更有效的人群管理策略、优化人群流动并提升整体人群体验至关重要。然而，在微观层面人工识别推挤行为颇具挑战，现有自动化方法无法检测此类微观行为。为此，本文提出一种新颖的自动化框架，用于在微观层面识别人群视频中的推挤行为。该框架包含两大核心组件：i) 特征提取与 ii) 视频标注。在特征提取组件中，我们开发了一种基于Voronoi的新方法，用于确定输入视频中每个个体的局部关联区域。随后，这些区域被输入EfficientNetV1B0卷积神经网络，以提取每个个体随时间演化的深度特征。在第二组件中，采用全连接层与Sigmoid激活函数的组合，分析这些深度特征并标注视频中涉及推挤的个体。该框架基于通过六项真实世界实验创建的新数据集进行训练与评估，数据集包含对应的真实标注。实验结果表明，相比用于对比分析的七种基线方法，所提框架表现更优。