Amanote Research

Amanote Research

    RegisterSign In

Human Action Recognition Based on 3D Convolution Neural Networks From RGBD Videos

doi 10.24132/csrn.2018.2803.4
Full Text
Open PDF
Abstract

Available in full text

Date

January 1, 2018

Authors
Martin LambersHendrik SommerhoffAndreas Kolb
Publisher

Západočeská univerzita


Related search

Human Action Recognition Based on Convolutional Neural Networks With a Convolutional Auto-Encoder

2016English

Skeleton Based View Invariant Human Action Recognition Using Convolutional Neural Networks

International Journal of Recent Technology and Engineering
EngineeringManagement of TechnologyInnovation
2019English

Chinese Font Recognition Based on Convolution Neural Network

2018English

Automatic Human Activity Segmentation and Labeling in RGBD Videos

6th International Conference on Research into Design, ICoRD 2017
Decision SciencesComputer Science
2016English

Differential Recurrent Neural Networks for Action Recognition

2015English

Human Shape Recognition From Snakes Using Neural Networks

English

Action Recognition From 3D Skeleton Sequences Using Deep Networks on Lie Group Features

2018English

RGBD Human Action Recognition Using Multi-Features Combination and K-Nearest Neighbors Classification

International Journal of Advanced Computer Science and Applications
Computer Science
2017English

Diseased Portion Cassification & Recognition of Cotton Plants Using Convolution Neural Networks

International Journal of Engineering and Advanced Technology
EngineeringComputer Science ApplicationsEnvironmental Engineering
2019English

Amanote Research

Note-taking for researchers

Follow Amanote

© 2025 Amaplex Software S.P.R.L. All rights reserved.

Privacy PolicyRefund Policy