Research on Service Behavior Identification of Power Supply Business Hall under Digital Background
In order to solve the problem of complex scenarios and difficult service behavior recognition in power service industry,a service behavior recognition fusion network for power supply business hall is proposed.The network mainly includes spatio-temporal partition network model and improved C3D network model.Firstly,optical flow frames and RGB frames are extracted from video.Secondly,the extracted optical flow frames and RGB frames are brought into the spatiotemporal segmentation net-work and the improved C3D network for training,so as to effectively extract action features and image features.Finally,in the classification layer,the recognition accuracy of each network for each type of service action is calculated,the weight is deter-mined by Softmax formula,and the final action recognition result is obtained.In the simulation phase,the service video data set provided by China Southern Power Grid Corporation is taken as an example to verify the proposed model.The simulation results show that the recognition accuracy of the proposed method is 98.99％,the recall rate is 90.2％,and the f score is 94.39％.The simulation results further verify that the proposed model has high accuracy and stable recognition rate for service behavior.
power systembusiness hallbehavior identificationoptical flowspatiotemporal segmentation
万方数据
维普
