Visual front-end for underwater scene change detection and environment monitoring by the autonomous drone

Boguslaw Cyganek; Bogdan Smolka

doi:10.1117/12.2519390

14 May 2019 Visual front-end for underwater scene change detection and environment monitoring by the autonomous drone

Boguslaw Cyganek, Bogdan Smolka

Proceedings Volume 10996, Real-Time Image Processing and Deep Learning 2019; 109960U (2019) https://doi.org/10.1117/12.2519390
Event: SPIE Defense + Commercial Sensing, 2019, Baltimore, MD, United States

Abstract

Autonomous underwater drone operation requires on-line analysis of signals coming from various sensors. In this paper we focus on design of the visual front-end of an underwater drone which is optimized for abrupt signal change detection for help in maneuvering and underwater object search operations. The proposed method relies on tensor space comparison with the chordal kernel function. This kernel measures a distance expressed as principal angles on Grassman manifolds of unfolded tensors. Although tested on color videos, the method can be scaled to accept more signal types in the input tensors. Experiments show promising results.

Citation Download Citation

Boguslaw Cyganek and Bogdan Smolka "Visual front-end for underwater scene change detection and environment monitoring by the autonomous drone", Proc. SPIE 10996, Real-Time Image Processing and Deep Learning 2019, 109960U (14 May 2019); https://doi.org/10.1117/12.2519390

ACCESS THE FULL ARTICLE

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