Behavior recognition in infrared video based on global bilinear attention
To address the problem that infrared video lacks texture detail features which is difficult to balance the com-putational complexity and recognition accuracy in human behavior recognition,a global bilinear attention-based behav-ior recognition method for infrared video is proposed in this paper.Firstly,in order to efficiently compute human be-havior in infrared video,a joint extraction module based on a two-stage detection network is designed to obtain human joint point information,and the resulting 3D heat map of joints is innovatively used as an input feature for the human behaviour recognition network in infrared video.Moreover,to further improve the recognition accuracy on the basis of lightweight computation,a global bilinear attention-based 3D convolutional network is proposed to enhance the atten-tion from both spatial and channel dimensions modeling capability to capture global structural information.The experi-mental results on the InfAR and IITR-IAR datasets demonstrate the effectiveness of the method in infrared video be-havior recognition.
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