Lightweight Human Pose Estimation Algorithm Based on Improved FastPose
1. School of Automation and Electrical Engineering, Dalian Jiaotong University, Dalian 116028, China
2. Tianjin Rail Transit Group Corporation, Tianjin 300380, China
3. School of Computer and Communication Engineering, Dalian Jiaotong University, Dalian 116028, China
| Abstract: | The performance of human pose estimation network model is gradually improved, and the over-deep network structure brings a large number of parameters and complex calculation. To solve these problems, the FastPose-Lite lightweight human pose estimation network is proposed, which is composed of GSE-ResNet feature extraction network, up-sampling DUC module and CBAM module. The basic GBNK module of GSE-ResNet feature extraction network is composed of Ghost module and SE module. On the one hand, Ghost module is proposed to replace the traditional convolutional module in order to reduce the number of parameters and calculation. On the other hand, in order to keep the performance of the network model unchanged, SE attention mechanism module is introduced. In order to enhance the processing ability of the network model to the feature information in both spatial and channel aspects, CBAM module is introduced into the up-sampling DUC module to reduce the loss in the up-sampling process. The experimental results on the COCO dataset show that the proposed FastPose-Lite reduce the number of parameters and calculation by 51.4% and 50.8% respectively compared with the FastPose network model. Compared with the common popular network models such as SHN, CPN, and SimpleBaseline, the FastPose-Lite network model not only has fewer parameters and computations, but also has higher prediction accuracy. |
| Keywords: | Human Pose Estimation; FastPose; Ghost Module; Attention Mechanism; Lightweight |
| DOI: | 10.57237/j.cst.2023.02.003 |
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Received: 1 March 2023
Accepted: 6 April 2023
Published Online: 7 April 2023
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