A LIGHTWEIGHT MULTI-PERSON POSE ESTIMATION SCHEME BASED ON JETSON NANO
Lei Liu
13605545615@163.comCollege of Computing and Information Technologies, National University Manila (Philippines)
https://orcid.org/0000-0002-1807-6906
Eric B. Blancaflor
Mapua University, School of Information Technology, Philippines (Philippines)
Mideth Abisado
National University, College of Computing and Information Technologies, Philippines (Philippines)
https://orcid.org/0000-0003-4215-7260
Abstract
As the basic technology of human action recognition, pose estimation is attracting more and more researchers' attention, while edge application scenarios pose a higher challenge. This paper proposes a lightweight multi-person pose estimation scheme to meet the needs of real-time human action recognition on the edge end. This scheme uses AlphaPose to extract human skeleton nodes, and adds ResNet and Dense Upsampling Revolution to improve its accuracy. Meanwhile, we use YOLO to enhance AlphaPose’s support for multi-person pose estimation, and optimize the proposed model with TensorRT. In addition, this paper sets Jetson Nano as the Edge AI deployment device of the proposed model and successfully realizes the model migration to the edge end. The experimental results show that the speed of the optimized object detection model can reach 20 FPS, and the optimized multi-person pose estimation model can reach 10 FPS. With the image resolution of 320×240, the model’s accuracy is 73.2%, which can meet the real-time requirements. In short, our scheme can provide a basis for lightweight multi-person action recognition scheme on the edge end.
Supporting Agencies
Keywords:
human pose estimation, lightweight model, Edge AI, deep learning, computer visionReferences
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Authors
Lei Liu13605545615@163.com
College of Computing and Information Technologies, National University Manila Philippines
https://orcid.org/0000-0002-1807-6906
Authors
Eric B. BlancaflorMapua University, School of Information Technology, Philippines Philippines
Authors
Mideth AbisadoNational University, College of Computing and Information Technologies, Philippines Philippines
https://orcid.org/0000-0003-4215-7260
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