End-to-End Feature Pyramid Network for Real-Time Multi-Person Pose Estimation
2019 16th International Conference on Machine Vision Applications (MVA)(2019)
摘要
In computer vision, pose estimation system is widely used to construct human body transformation. However, it is hard to achieve these targets together: stable real-time speed, variance human number and high accuracy. This paper proposes an end-to-end pose estimation network. It contains a neural network friendly representation of human pose. Then it proposes a correspond real-time end-to-end pose estimation network based on feature pyramid network structure with attention-based detection modules. This network can detect multiple humans in more than 60 fps with 384 × 384 resolution on GTX 1070 with affordable accuracy. This work shows the potential of this network structure can perform both faster and better compared with state-of-the-art results.
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关键词
computer vision,human body transformation,variance human number,feature pyramid network structure,attention-based detection modules,pose estimation network,multiperson pose estimation system,neural network
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