A lightweight OpenPose posture detection model for edge devices
In order to achieve real-time human behavior recognition on edge devices with low computational power while keeping a balance between real-time performance and recognition effectiveness,we proposed an improved lightweight OpenPose posture detection model.The model replaces the original backbone feature extraction network with mobile networks,uses inverted residual structures in the shallow layers of the feature extraction network to reduce computational complexity,introduces convolutional block attention module in the deep layers to adjust the weights of deep feature information,and fuses shallow and deep feature information after merging them before feeding them into the convolutional network for the concatenation of skeleton keypoints.This effectively integrates both shallow and deep feature information.Validation results on the COCO dataset show that compared to the original model,the improved model achieves a 2.8%increase in correct keypoint percentage and a 2.0%increase in average precision.Using the improved model as a pre-trained model,skeletal keypoints are labeled on a behavioral dataset for classification training.When deploying the trained model on edge devices,even with a slight decrease in operating speed on edge devices,the accuracy of human behavior recognition reached 96.4%,effectively realizing posture detection and human behavior recognition on edge devices.
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