Improved Lightweight Attitude Estimation Method ULPN Based on LPN
In the process of human pose estimation,complex network structures are often adopted to achieve better prediction performance,but the actual inference speed of the model is slow.In response to this problem,the paper proposes a new lightweight human pose estimation network ULPN,which uses the lightweight human pose estimation network LPN as the basis,and uses an im-proved lightweight network and a new lightweight bottleneck block to improve model inference efficiency.The paper proposes a new lightweight bottleneck block based on the Ghost convolution module and the attention mechanism block GCB to reduce redundant feature maps and simultaneously model the global.Using a continuous convolutional layer instead of a pooling layer can save more ef-fective information,and grouped convolution is used to reduce the computational load of the model.At the same time,a lightweight human pose estimation network ULPN is proposed based on the lightweight bottleneck block.With similar prediction accuracy,the algorithm can effectively reduce the computational load of the model,and better perform real-time human pose estimation.
deep learningpose estimationlightweight modelinference speedpredict accuracy
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