Research on Improved Moving Human Body Recognition Based on FlowNet2.0
Aiming at the problem that the existing Two-Stream Convolutional Neural Networks cannot quickly and accurately identify human body information because the human body moves fast in motion,an improved human recognition detection method based on FlowNet2.0 network is proposed,which can effectively enhance the network's ability to extract appearance information and posture features by introducing Self-Attention into the input channels of each video frame of FlowNet2.0 network,so as to better describe moving targets.Finally,the model is trained on the HDBM51 dataset,and the experimental results show that the improved FlowNet2.0 network has achieved significant improvement results.This study provides an effective solution to solve the problems of human recognition during action.
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