13 papers with code • 1 benchmarks • 3 datasets
To achieve this, we model various exploratory inspection and diagnostic tasks for deep learning training processes as specifications for streams using a map-reduce paradigm with which many data scientists are already familiar.
Recent approaches in depth-based human activity analysis achieved outstanding performance and proved the effectiveness of 3D representation for classification of action classes.
In this work, we propose to use a new class of models known as Temporal Convolutional Neural Networks (TCN) for 3D human action recognition.
Ranked #1 on Multimodal Activity Recognition on EV-Action
Each available 3DV voxel intrinsically involves 3D spatial and motion feature jointly.
The proposed representation has the advantage of combining the use of reference joints and a tree structure skeleton.
Ranked #6 on Action Recognition on NTU RGB+D 120
Due to the availability of large-scale skeleton datasets, 3D human action recognition has recently called the attention of computer vision community.
Ranked #7 on Action Recognition on NTU RGB+D 120
The proposed method achieved state-of-the-art performance on NTU RGB+D dataset for 3D human action analysis.
Ranked #67 on Skeleton Based Action Recognition on NTU RGB+D