LOGIN TO YOUR ACCOUNT

Username
Password
Remember Me
Or use your Academic/Social account:

Congratulations!

You have just completed your registration at OpenAire.

Before you can login to the site, you will need to activate your account. An e-mail will be sent to you with the proper instructions.

Important!

Please note that this site is currently undergoing Beta testing.
Any new content you create is not guaranteed to be present to the final version of the site upon release.

Thank you for your patience,
OpenAire Dev Team.

Close This Message

CREATE AN ACCOUNT

Name:
Username:
Password:
Verify Password:
E-mail:
Verify E-mail:
*All Fields Are Required.
Please Verify You Are Human:

OpenAIRE is about to release its new face with lots of new content and services.
During September, you may notice downtime in services, while some functionalities (e.g. user registration, validation, claiming) will be temporarily disabled.
We apologize for the inconvenience, please stay tuned!
For further information please contact helpdesk[at]openaire.eu

fbtwitterlinkedinvimeoflicker grey 14rssslideshare1
M.Papakostas; T. Giannakopoulos; F. Makedon; V. Karkaletsis (2017)
Types: Conference object
Subjects:
This paper proposes a deep learning classification method for frame-wise recognition of human activities, using raw color (RGB) information. In particular, we present a Convolutional Neural Network (CNN) classification approach for recognising three basic motion activity classes, that cover the vast majority of human activities in the context of a home monitoring environment, namely: sitting, walking and standing up. A real-world fully annotated dataset has been compiled, in the context of an assisted living home environment. Through extensive experimentation we have highlighted the benefits of deep learning architectures against traditional shallow classifiers functioning on hand-crafted features, on the task of activity recognition. Our approach proves the robustness and the quality of CNN classifiers that lies on learning highly invariant features. Our ultimate goal is to tackle the challenging task of activity recognition in environments that are characterized with high levels of inherent noise.
  • No references.
  • No related research data.
  • No similar publications.

Share - Bookmark

Download from

Funded by projects

  • EC | RADIO

Cite this article

Collected from

Cookies make it easier for us to provide you with our services. With the usage of our services you permit us to use cookies.
More information Ok