A method for recognizing the sleeping-on-duty behavior of air traffic controllers based on a two-stream adaptive graph convolutional network is proposed to reduce control errors,ensure flight safety,and enhance the ability of civil aviation operation safety management.Firstly,the OpenPose algorithm is utilized to extract the skeleton key points of the controller in each frame of the video.Then,the matching optimization and clustering algorithms are applied to cluster and combine the key points to obtain the skeleton joint graph of the controller,and the same joint points of different frames are connected to obtain the controller skeletal spatial-temporal graph.The controller skeletal spatial-temporal graph is then transformed into joint flow data and bone flow data,and the two-stream network is used to process the joint flow information and bone flow information respectively,achieving full extraction of the first-order and second-order information of the skeleton data.Next,the adaptive learning bone topology connection matrix is employed to explore the functional connection between different joints of the controller,while the STC attention mechanism is introduced in the convolutional layer to enhance the ability of the sleeping-on-duty behavior recognition model to extract important information in the time,space,and channel dimensions.Finally,the Controller Working Status Dataset is collected for model training,and the recognition of three sleeping on duty behavior,nodding off,falling asleep with head down,and falling asleep with head tilted back,is achieved on the test set,verifying the effectiveness of the adaptive graph convolutional network and two-stream network design.Experimental results show that compared to the spatial-temporal graph convolutional network.The proposed method achieves a more significant improvement in the recognition accuracy of sleeping on duty behavior,with a recognition accuracy of 95.03％,which can strengthen the effective control of the working behavior of controllers and enhance the ability of civil aviation operation safety management.
关键词
安全社会工程/睡岗行为/空中交通管制员/自适应图卷积网络/行为识别
Key words
safety social engineering/sleeping on duty behavior/air traffic controller/Adaptive Graph Convolution Network(AGCN)/behavior recognition
维普
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