Mr. Zhong Li Profile

Zhong Li

at National Univ. of Defence Technology

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 | 27 November 2019 Paper

A new gait energy image based on mask processing for pedestrian gait recognition

Zhong Li, Jiulong Xiong, Xiangbin Ye

Proceedings Volume 11321, 113212A (2019) https://doi.org/10.1117/12.2547870

KEYWORDS: Gait analysis, Sensors, Thin film coatings, Feature extraction, Mathematical modeling, Image processing, Model-based design, Data modeling, Convolution, Binary data

Read Abstract +

Under a more realistic experimental setup, the performance of the existing gait recognition approaches would drop drastically. Because pedestrians are mostly under different and unknown covariate conditions. Thus, the influence caused by changes of clothing and carrying on profile of pedestrians is the main obstacle of gait recognition. In this paper, we propose a new Gait Energy Image based on mask processing (MP-GEI) to reduce the influence of covariate conditions. Firstly, we calculate the Gait Energy Image (GEI) and its synthetic average template which includes the various features of 124 subjects under different covariate conditions from five views (54°, 72°, 90°, 108°, 126°). Secondly, we propose Gait Entropy Image (GEnI) and calculate its synthetic average template (T-GEnI). Thirdly, we calculate the mask representing the dynamic feature areas in T-GEnI by setting the threshold. Finally, we use parts of the mask to remove the irrelevant gait information in GEI. In this work, we explore the performance of MP-GEI with two models based on convolution neural network (CNN), and experiments are carried out on the CASIA Dataset B. Our results demonstrate that the proposed approach achieves better correct classification rate compared with GEI when pedestrians are under different and unknown covariate conditions. In addition, using the pre-trained VGG-16 model to extract deep features for recognition is more effective than fine-tuning the pre-trained VGG-16 model.

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