基于生成对抗网络的变电站工作人员行为预测的方法

Prediction Method for the Behavior of Substation Staff Based on Generative Adversarial Network

提出了一种基于视频场景、利用生成对抗网络来整合全局和局部信息的变电站工作人员异常行为预测方法。在变电站中,此方法可以用来对运检人员在作业过程中可能触发危险的举动发出及时警告,从而为运检人员的生命安全提供重要保障。人类行为预测任务旨在基于给定的行为视频帧来预测未来的行为视频帧。考虑到人类行为视频中既包含相对稳定不变的场景信息,又包含时变复杂的人类行为信息,本方法首先使用全局生成对抗网络来生成视频场景以及粗糙的人体轮廓;然后再利用局部生成对抗网络来进一步优化视频中的人类行为细节。实验表明,与现有的仅利用单一模型来实现像素级别的行为预测的方法相比,本文所提出的全局和局部生成相结合的方法可以更好地捕获视频中人类的空间外观和时序动态。

In this paper,a prediction approach for abnormal behavior of substation staff is proposed by combining global and local information using generative adversarial networks based on video scenes.In substations,this method can be used to issue timely warnings to operating and maintenance personnel who may trigger dangerous actions in the process of operation,which provides an important guarantee for the safety of the workers.The prediction task of human behavior aims to predict future action video frames based on given of human action video frames.Considering that human action videos contain both background scenes which are relatively time-invariant among frames,and alseo human actions information which are time-varying components in videos,a global generative adversarial network is used to model the time-invariant background and coarse human profiles;Then a local generative adversarial network is utilized to furtherly refine the time-varying human body details in the video.Compared with existing methods which only obtain pixel-level action predictions,these experiments indicate that our global and local combined approach can capture both spatial appearances and temporal dynamics of videos simultaneously.