Papers with Code 아카이브
RF-based Pose Estimation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 0
- 평가표
- 1
- 보존된 평가 기록
- 3
Detect human actions through walls and occlusions, and in poor lighting conditions. Taking radio frequency (RF) signals as input (e.g. Wifi), generating 3D human skeletons as an intermediate representation, and recognizing actions and interactions.
See e.g. RF-Pose from MIT for a good illustration of the approach http://rfpose.csail.mit.edu/
<span style="color:grey; opacity: 0.6">( Image credit: Making the Invisible Visible )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/geekfeiw/wifiperson
공식 코드https://github.com/hikvision-research/skelact
공식 코드https://github.com/fandulu/Keras-for-Co-occurrence-Feature-Learning-from-Skeleton-Data-for-Action-Recognition
https://github.com/hhe-distance/AIF-CNN
https://github.com/huguyuehuhu/HCN-pytorch
https://github.com/maxstrobel/HCN-PrototypeLoss-PyTorch
https://github.com/natepuppy/HCN-pytorch
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.