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2 results about "Movement recognition" patented technology

Viewpoint adjustment-based graph convolution cycle network skeleton action recognition method and system

ActiveCN111339942ASolve the problem of viewing angleRealize modelingBiometric pattern recognitionNeural architecturesTime informationSkeletal movement
The invention provides a viewpoint adjustment-based graph convolution loop network skeleton action recognition method and system, relates to the technical field of action recognition, and solves the problem of recognition accuracy reduction caused by different observation visual angles. Utilizing the trained graph convolution recurrent neural network, and taking the preprocessed data as input to obtain spatiotemporal information of the bone data; a Softmax function is adopted, the obtained space-time information serves as input, and a skeletal movement classification result is obtained; the method integrates the advantages of the graph convolution network and the cyclic network, achieves the modeling of the time and space information of the skeleton data, can further improve the accuracy of movement recognition on the basis of an LSTM network movement recognition method, is universal in behavior recognition based on a skeleton data set, and is wide in application prospect.
Owner:SHANDONG UNIV

Audition and vision collaborative humanoid robot head orientation method

PendingCN113910217AImprove interactive experienceProgramme-controlled manipulatorHumanoid robot naoVisual perception
The invention provides an audition and vision collaborative humanoid robot head orientation method. The method is characterized by mainly comprising the following steps of firstly, acquiring a voice signal through a microphone array and recording time; recognizing by using a human voice recognition method, and counting the number of microphones for collecting human voice; setting a candidate target orientation according to time when the number of the voice microphones is greater than 1; then rotating a camera and acquiring a video; obtaining human face image frames from the video, obtaining a human face recognition result through recognition by means of a human face recognition method, and counting the number of human faces; when the number of the human faces is greater than 1, calculating human face areas and sorting the human face areas; setting candidate target human faces according to the ranking; then, achieving lip movement recognition through a lip movement detection algorithm; setting a target orientation according to the area when the number of the human faces with lip movement is greater than 1; and finally rotating the head of the humanoid robot to realize orientation. According to the method, the humanoid robot can distinguish the human voice from other voices and can accurately interact with the target human voice.
Owner:FUDAN UNIV
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