computer-vision-7
RF-based Pose Estimation
3260 papers • 126 benchmarks • 313 datasets
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/ ( Image credit: Making the Invisible Visible )
(Image credit: Papersgraph)
Benchmarks
These leaderboards are used to track progress in rf-based-pose-estimation-7
Trend
Dataset
Best Model
Actions
RF-MMD
RF-MMD
RF-MMD
RF-MMD
Libraries
iUse these libraries to find rf-based-pose-estimation-7 models and implementations
Datasets
No datasets available.
Subtasks
No subtasks available.
Most implemented papers
Co-occurrence Feature Learning from Skeleton Data for Action Recognition and Detection with Hierarchical Aggregation
C. Li, Shiliang Pu, Qiaoyong Zhong, Di Xie•Mon Apr 16 2018
This paper introduces a global spatial aggregation scheme, which is able to learn superior joint co-occurrence features over local aggregation, and consistently outperforms other state-of-the-arts on action recognition and detection benchmarks like NTU RGB+D, SBU Kinect Interaction and PKU-MMD.
572