ArticleSensors (Basel, Switzerland)2019
Spatio⁻Temporal Image Representation of 3D Skeletal Movements for View-Invariant Action Recognition with Deep Convolutional Neural Networks.
Article in Sensors (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
7 citing papers in PubMed.
- A Deep Sequence Learning Framework for Action Recognition in Small-Scale Depth Video Dataset.Sensors (Basel, Switzerland) · 2022Article
- Skeleton Driven Action Recognition Using an Image-Based Spatial-Temporal Representation and Convolution Neural Network.Sensors (Basel, Switzerland) · 2021Article
- Detection of sitting posture using hierarchical image composition and deep learning.PeerJ. Computer science · 2021Article
- Application of Machine Learning in Air Hockey Interactive Control System.Sensors (Basel, Switzerland) · 2020Article
- Prediction of Human Activities Based on a New Structure of Skeleton Features and Deep Learning Model.Sensors (Basel, Switzerland) · 2020Article
- Keys for Action: An Efficient Keyframe-Based Approach for 3D Action Recognition Using a Deep Neural Network.Sensors (Basel, Switzerland) · 2020Article
- A Unified Deep Framework for Joint 3D Pose Estimation and Action Recognition from a Single RGB Camera.Sensors (Basel, Switzerland) · 2020Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Designing motion representations for 3D human action recognition from skeleton sequences is an important yet challenging task. An effective representation should be robust to noise, invariant to viewpoint changes and result in a good performance with low-computational demand. Two main challenges in this task include how to efficiently represent spatio-temporal patterns of skeletal movements and how to learn their discriminative features for classification tasks. This paper presents a novel skeleton-based representation and a deep learning framework for 3D action recognition using RGB-D sensors. We propose to build an action map called SPMF (
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.