Two-person interaction recognition based on the interactive relationship hypergraph convolution network model
To enhance the security of schools,shopping malls,and other public places,it is important to achieve auto-matic identification of abnormal two-person interaction behaviors,such as stealing,robbing,fighting,and assaulting,in surveillance videos.However,the current behavior recognition method based on joint data in graph creation neglects the two-person interaction information as well as the interaction relationship between the single unnatural connection joints.To address this issue,a two-person interaction behavior recognition model based on the interactive relationship hyper-graph convolution network is proposed to model and identify human interactions.First,the corresponding single hyper-graph and two-person interaction graph are created for the joint-point data of each frame,where the hypergraph makes the information of multiple unnaturally connected nodes interchangeable at the same time,and the interaction graph em-phasizes the interaction strength between nodes.The above-constructed graph models are fed into the spatiotemporal graph convolution to model the spatial and temporal information separately,and lastly,the recognition results are ac-quired by the SoftMax classifier.The benefits of the proposed algorithm framework are that the interactive relationship between two persons,the structural relationship between unnatural connections,and the flexible motion characteristics of limbs are regarded in the graph construction process.Tests on the NTU data set demonstrate that the algorithm at-tains a correct recognition rate of 97.36%.The findings indicate that the network model enhances the ability to represent the characteristics of two-person interaction and has better recognition performance than the current models.
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