Human Pose and Action Recognition
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Scope of this subject
This cluster of papers focuses on the development and application of deep learning techniques for human action recognition and pose estimation. It covers topics such as spatiotemporal feature learning, convolutional networks, 3D human pose estimation, skeleton-based recognition, and video classification. The research aims to advance the understanding and accurate detection of human actions in various environments.
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Research vocabulary
human action recognition; human pose estimation; video action recognition; skeleton-based action recognition; 3D human pose estimation; OpenPose; video classification; UCF101; temporal action localization; markerless motion capture; real-time pose estimation; violence detection; 3D pose estimation; untrimmed video; multi-person pose estimation; human motion recognition; action classification; HMDB51; sports video analysis; Human3.6M; egocentric video; spatio-temporal graph convolutional network; two-stream network; human-object interaction detection; NTU RGB+D
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Numeric address and provenance
Morton address: 36174758274582615 · morton4x16-v1
Coordinates: (3, 17, 1707, 10812). The address interleaves categorical identifier bits. Nearby addresses are not guaranteed to describe semantically similar subjects.
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