Based on the analysis of existing 2D multi human body pose recognition methods,an improved composite field for 2D multi human body pose recognition is proposed to address the issues of long time consumption and low accuracy.In-troducing a hollow convolutional module to reduce the number of parameters while improving model accuracy,and introdu-cing shuffleNet V2 network to replace the backbone network ResNet to improve model recognition speed.Analyze the aver-age accuracy,average recall rate,and running time of the proposed method through experiments.The results show that com-pared with conventional methods,the proposed method has higher recognition accuracy and speed for 2D multi person human pose recognition,with an average recognition time of 75ms for 1-8 people.This provides a certain reference for the re-search of computer vision.
body posturecomposite fieldatrous convolution moduleShuffleNet V2 network2D multiple persons
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