Behavior recognition method based on dual-channel depth separable convolution ofskeleton data

• Main Authors: ZHANG GUODAO, QIU FEIYUE, WANG LIPING, CHEN HONG, GUO HAIDONG, KONG DEWEI, JIANG BIJUN
• Format: Patent
• Language: Chinese; English
• Published: 15.12.2020
• Subjects: CALCULATING; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS; COMPUTING; COUNTING; HANDLING RECORD CARRIERS; PHYSICS; PRESENTATION OF DATA; RECOGNITION OF DATA; RECORD CARRIERS

The invention discloses a behavior recognition method based on dual-channel depth separable convolution of skeleton data, and belongs to the technical field of human body posture behavior recognition.The method comprises the following steps: 1, acquiring human body behavior posture joint skeleton point data; 2, processing the skeleton point data to extract behavior space features; 3, constructinga D2SE dual-channel depth separable convolution layer, and extracting behavior time features in a time dimension; 4, superposing the space information on the graph convolution and the time information on a D2SE network layer to extract the space-time information of the attitude behavior; and 5, using a ReLu function to obtain bone movement classification. The GCN network layer and the D2SE network layer are used, spatial image convolution is used for human body posture behavior skeleton data to extract spatial information. Based on double channels, extra complexity is not introduced when theperformance of a convolutio