Ms. Sara Rojas Martinez Profile

Sara Rojas Martinez

SPIE Involvement:

Author

Publications (1)

This will count as one of your downloads.

You will have access to both the presentation and article (if available).

DOWNLOAD NOW

This content is available for download via your institution's subscription. To access this item, please sign in to your personal account.

Email or Username Forgot your username?

Password Forgot your password?

Show

Keep me signed in

No SPIE account? Create an account

Proceedings Article | 3 January 2020 Paper

Precise human pose estimation based on two-dimensional images for kinematic analysis

Sara Rojas Martínez, Diana Sofia Herrera, Pablo Arbeláez

Proceedings Volume 11330, 113300F (2020) https://doi.org/10.1117/12.2542539

KEYWORDS: Algorithm development, Convolution, Image processing, Kinematics, Network architectures, Error analysis, Image analysis, Electroluminescent displays, RGB color model, Sensors

Read Abstract +

Human Pose Estimation (HPE) is a Computer Vision problem that has become increasingly popular over the last few years, with multiple applications in the medical field such as therapy using virtual and augmented reality, robot caregivers, virtual physical therapy and kinematic analysis. Nevertheless, all the machine learning algorithms developed for these applications are trained in small datasets with images captured in constrained scenarios and with information given by sensors, bounding the applicability of these methods. We developed a simple yet useful deep learning algorithm for Human Pose Estimation that uses as input only an image of a scene with people. The estimated position of the joints and body parts can be used to retrieve basic kinematic information from the people on the image that can be applied to the aforementioned medical applications. We focus on overcoming the limit of Human Pose Estimation algorithms due to jittering, aiming to preserve more precise pixel location. Thus, we explore different novel approaches to improve the precision of the existing state- of-the-art algorithms in keypoint estimation and evaluate them on COCO keypoint dataset, outperforming the current top methods. We hope our algorithm encourages the academic community to develop simpler but precise HPE algorithms for medical applications based on RGB images.

My Library

You currently do not have any folders to save your paper to! Create a new folder below.

Folder Name

Folder Description

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks. You are receiving this notice because your organization may not have SPIE eBooks access.*

*Shibboleth/Open Athens users─please sign in to access your institution's subscriptions.

To obtain this item, you may purchase the complete book in print or electronic format on SPIE.org.

ORGANIZATIONAL
Sign in with credentials provided by your organization.

Organizational Username

Organizational Password

Show/Hide Password

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members:

Non-members: ADD TO CART

Keywords/Phrases

Search In:

Publication Years