Mr. Nicholas Warren Profile

Nicholas Warren

at Texas State Univ.

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 | 10 May 2019 Presentation + Paper

Identifying maritime vessels at multiple levels of descriptions using deep features

David Bo Heyse, Nicholas Warren, Jelena Tešić

Proceedings Volume 11006, 1100616 (2019) https://doi.org/10.1117/12.2519248

KEYWORDS: Maritime surveillance, Data modeling, Neural networks, Sensors, Performance modeling, Video, Machine learning, Machine vision, Computing systems

Read Abstract +

Varying resolution quality of operational data, size of targets, view occlusions, and large variation in sensors due to nature of overhead systems as compared to consumer devices contribute to degradation of the maritime vessel identification. We exploit the maritime domain characteristics to optimize and refine the deep learning Mask-RCNN framework for training generic maritime vessel classes. Maritime domain, compared to consumer domain, lack alternative targets that would be incorrectly associated as maritime vehicles: this allows us to relax the parameter constraints learned on urban natural scenes in consumer photos, adjust parameters of the model inference, and achieve robust performance and high AP measure for transfer learning scenarios. In this paper, we build upon this robust localization work, and extend our transfer learning work to new domains and datasets. We propose new approach for identifying specific category of maritime vessels and build a refined multi-label classifier that is based on deep Mask-RCNN features. The classifier is designed to be robust to domain transfer (e.g. different overhead maritime video feed), and to the noise in the data annotation (e.g. vessel is not correctly marked or label is ambiguous). We demonstrate superior category classification results of this low shot learning approach on publicly available MarDCT dataset.

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