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Found 10 papers, 2 papers with code
NTU RGB+D 120: A Large-Scale Benchmark for 3D Human Activity Understanding
Research on depth-based human activity analysis achieved outstanding performance and demonstrated the effectiveness of 3D representation for action recognition.
Ranked #5 on
One-Shot 3D Action Recognition
on NTU RGB+D 120
Skeleton-Based Online Action Prediction Using Scale Selection Network
Since there are significant temporal scale variations in the observed part of the ongoing action at different time steps, a novel window scale selection method is proposed to make our network focus on the performed part of the ongoing action and try to suppress the possible incoming interference from the previous actions at each step.
Ranked #64 on
Skeleton Based Action Recognition
on NTU RGB+D 120
Feature Boosting Network For 3D Pose Estimation
Learning a set of features that are reliable and discriminatively representative of the pose of a hand (or body) part is difficult due to the ambiguities, texture and illumination variation, and self-occlusion in the real application of 3D pose estimation.
SSNet: Scale Selection Network for Online 3D Action Prediction
As there are significant temporal scale variations of the observed part of the ongoing action at different progress levels, we propose a novel window scale selection scheme to make our network focus on the performed part of the ongoing action and try to suppress the noise from the previous actions at each time step.
Skeleton-Based Action Recognition Using Spatio-Temporal LSTM Network with Trust Gates
Skeleton-based human action recognition has attracted a lot of research attention during the past few years.
Ranked #6 on
One-Shot 3D Action Recognition
on NTU RGB+D 120
Spatio-Temporal LSTM with Trust Gates for 3D Human Action Recognition
To handle the noise and occlusion in 3D skeleton data, we introduce new gating mechanism within LSTM to learn the reliability of the sequential input data and accordingly adjust its effect on updating the long-term context information stored in the memory cell.
Ranked #8 on
Skeleton Based Action Recognition
on SBU
NTU RGB+D: A Large Scale Dataset for 3D Human Activity Analysis
Recent approaches in depth-based human activity analysis achieved outstanding performance and proved the effectiveness of 3D representation for classification of action classes.
Deep Multimodal Feature Analysis for Action Recognition in RGB+D Videos
Single modality action recognition on RGB or depth sequences has been extensively explored recently.
Recent Advances in Convolutional Neural Networks
In the last few years, deep learning has led to very good performance on a variety of problems, such as visual recognition, speech recognition and natural language processing.
Multimodal Multipart Learning for Action Recognition in Depth Videos
We propose a joint sparse regression based learning method which utilizes the structured sparsity to model each action as a combination of multimodal features from a sparse set of body parts.
