Robust detection of small infrared objects in maritime scenarios using local minimum patterns and spatio-temporal context

Baojun Qi; Tao Wu; Hangen He

doi:10.1117/1.OE.51.2.027205

12 March 2012 Robust detection of small infrared objects in maritime scenarios using local minimum patterns and spatio-temporal context

Baojun Qi, Tao Wu, Hangen He

Author Affiliations +

Optical Engineering, Vol. 51, Issue 2, 027205 (March 2012). https://doi.org/10.1117/1.OE.51.2.027205

Abstract

Here, we describe a novel approach for small surface object detection with an onboard infrared (IR) camera working in maritime scenes. First, we propose a simple but effective tool called the local minimum patterns (LMP), which are theoretically the approximated coefficients of some stationary wavelet transforms, for single image background estimation. Second, potential objects are segmented by an adaptive threshold estimated from the saliency map, which is obtained by background subtraction. Using the LMP based wavelet transforms and the histogram of the saliency map, the threshold can be automatically determined by singularity analysis. Next, we localize potential objects by our proposed fast clustering algorithm, which, compared with popular K-Means, is much faster and less sensitive to noises. To make the surveillance system more reliable, we finally discuss how to integrate multiple cues, such as scene geometry constraints and spatio-temporal context, into detections by Bayesian inference. The proposed method has shown to be both effective and efficient by our extensive experiments on some challenging data sets with a competitive performance over some state-of-the-art techniques.

Citation Download Citation

Baojun Qi, Tao Wu, and Hangen He "Robust detection of small infrared objects in maritime scenarios using local minimum patterns and spatio-temporal context," Optical Engineering 51(2), 027205 (12 March 2012). https://doi.org/10.1117/1.OE.51.2.027205

Published: 12 March 2012

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