融合多特征与时间序列的人群行为识别模型

人群行为识别在公共安全等领域具有重要的应用价值.现有研究分别考虑了人群情绪、人群类型、人群密度以及人群社会文化环境等因素对于人群行为的影响,但少有综合考虑这些因素的模型,导致模型性能受限.本文综合考虑人群的物理特征、社交特征、情绪人格特征和文化背景特征之间的相关性,以及相结合之后对人群行为的影响,提出一种融合多特征与时间序列的人群行为识别模型.模型采用两个并行的网络层分别处理多特征相关性和时间序列依赖性对于人群行为的影响,同时为提高模型可解释性,网络层采用融合结构因果模型(SCM)与图神经网络(GNN)的因果图网络(CGN).通过在运动情感数据集(MED)上进行实验并与其他方法模型进行对比,证明了本文方法能够成功识别人群行为,并且优于目前最先进的方法.

Crowd Behavior Recognition Model Integrating Multi-feature and Time Series

Crowd behavior recognition has important application value in public safety and other fields. Existing studies have considered the influence of such factors on crowd behavior as crowd emotions, crowd types, crowd density, and social and cultural backgrounds of crowds separately, but few models comprehensively consider these factors, which limits model performance. This study comprehensively considers the correlation between the physical features, social features, emotional and personality features, and cultural background features of the crowd and the influence of the combination of these factors on crowd behavior. As a result, a crowd behavior recognition model that integrates multiple features and time series is proposed. The model uses two parallel network layers to deal with the influence of multi-feature correlation and time-series dependence on crowd behavior separately. Meanwhile, the network layer fuses the structural causal model (SCM) and the causal graph network (CGN) based on the graph neural network (GNN) to improve the interpretability of the model. The experiments on the motion and emotion dataset (MED) and the comparison with other state-of-the-art models demonstrate that the proposed method can successfully identify crowd behavior and outperform the state-of-the-art methods.