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Found 9 papers, 0 papers with code
Video-based Human Action Recognition using Deep Learning: A Review
Human action recognition is an important application domain in computer vision.
A Unified Deep Framework for Joint 3D Pose Estimation and Action Recognition from a Single RGB Camera
In the second, we deploy the Efficient Neural Architecture Search (ENAS) algorithm to find an optimal network architecture that is used for modeling the spatio-temporal evolution of the estimated 3D poses via an image-based intermediate representation and performing action recognition.
Ranked #80 on
3D Human Pose Estimation
on Human3.6M
A Deep Learning Approach for Real-Time 3D Human Action Recognition from Skeletal Data
We achieve promising results on real conditions of this dataset with the support of data augmentation and transfer learning techniques.
Learning to Recognize 3D Human Action from A New Skeleton-based Representation Using Deep Convolutional Neural Networks
In this paper we introduce a new skeleton-based representation for 3D action recognition in videos.
Skeletal Movement to Color Map: A Novel Representation for 3D Action Recognition with Inception Residual Networks
For learning and recognition tasks, we design and optimize new D-CNNs based on the idea of Inception Residual networks to predict actions from SPMFs.
Exploiting deep residual networks for human action recognition from skeletal data
In this paper, we investigate and apply deep ResNets for human action recognition using skeletal data provided by depth sensors.
Learning and Recognizing Human Action from Skeleton Movement with Deep Residual Neural Networks
Automatic human action recognition is indispensable for almost artificial intelligent systems such as video surveillance, human-computer interfaces, video retrieval, etc.
Exploiting Spatio-Temporal Structure with Recurrent Winner-Take-All Networks
We propose a convolutional recurrent neural network, with Winner-Take-All dropout for high dimensional unsupervised feature learning in multi-dimensional time series.
Computational Intelligence Challenges and Applications on Large-Scale Astronomical Time Series Databases
In this article we present an overview of machine learning and computational intelligence applications to TDA.
