Optimized feature-detection for on-board vision-based surveillance

Laetitia Gond; David Monnin; Armin Schneider

doi:10.1117/12.919730

10 May 2012 Optimized feature-detection for on-board vision-based surveillance

Laetitia Gond, David Monnin, Armin Schneider

Proceedings Volume 8357, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XVII; 83571N (2012) https://doi.org/10.1117/12.919730
Event: SPIE Defense, Security, and Sensing, 2012, Baltimore, Maryland, United States

Abstract

The detection and matching of robust features in images is an important step in many computer vision applications. In this paper, the importance of the keypoint detection algorithms and their inherent parameters in the particular context of an image-based change detection system for IED detection is studied. Through extensive application-oriented experiments, we draw an evaluation and comparison of the most popular feature detectors proposed by the computer vision community. We analyze how to automatically adjust these algorithms to changing imaging conditions and suggest improvements in order to achieve more exibility and robustness in their practical implementation.

Citation Download Citation

Laetitia Gond, David Monnin, and Armin Schneider "Optimized feature-detection for on-board vision-based surveillance", Proc. SPIE 8357, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XVII, 83571N (10 May 2012); https://doi.org/10.1117/12.919730

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