基于多级深度网络架构的群体行为分析模型研究

群体行为的多层次深度分析是行为识别领域亟待解决的重要问题。在深度神经网络研究的基础上,提出了群体行为识别的层级性分析模型。基于调控网络的迁移学习,实现了行为群体中多人体的时序一致性检测;通过融合时空特征学习,完成了群体行为中时长无约束的个体行为识别;通过场景中个体行为类别、交互场景上下文信息的融合,实现了对群体行为稳定有效的识别。在公用数据集上进行的大量实验表明,与现有方法相比,该模型在群体行为分析识别方面具有良好的效果。

Research on collective activity analysis model based on multilevel deep neural network architecture

Multi-level in-depth analysis of collective activity is an important issue to be solved in the field of activity recognition.Based on the research of deep neural network, this paper proposed a progressive hierarchical analysis model for activity recognition.Using the modulating network based on transfer learning, it detected multi-person with temporal consistency detection in the crowd.Through integrating spatio-temporal feature learning, it recognized the individual actions in the crowd with unconstrained action duration.Through integrating the individual action category, interaction context and scene context, it re-cognized the crowd activity steady and effectively.A large amount experiments on the benchmark data sets demonstrate that, compared with the current approaches, the proposed model achieves better performance on collective activity analysis and recognition.