Regular Articles

Approach for moving small target detection in infrared image sequence based on reinforcement learning

[+] Author Affiliations
Chuanyun Wang

Beihang University, School of Automation Science and Electrical Engineering, No. 37 Xueyuan Road, Haidian District, Beijing 100191, China

Shenyang Aerospace University, College of Computer Science, No. 37 Daoyi South Avenue, Shenbei New Area, Shenyang 110136, China

Shiyin Qin

Beihang University, School of Automation Science and Electrical Engineering, No. 37 Xueyuan Road, Haidian District, Beijing 100191, China

J. Electron. Imaging. 25(5), 053032 (Oct 18, 2016). doi:10.1117/1.JEI.25.5.053032
History: Received May 3, 2016; Accepted September 22, 2016
Text Size: A A A

Abstract.  Addressing the problems of moving small target detection in infrared image sequence caused by background clutter and target size variation with time, an approach for moving small target detection is proposed under a pipeline framework with an optimization strategy based on reinforcement learning. The pipeline framework is composed by pipeline establishment, target–background images separation, and target confirmation, in which the pipeline is established by designating several successive images with temporal sliding window, target–background images separation is dealt with low-rank and sparse matrix decomposition via robust principal component analysis, and target confirmation is achieved by employing a voting mechanism over more than one separated target images of the same input image. For unremitting optimization of target–background images separation, the weighting parameter of low-rank and sparse matrix decomposition is dynamically regulated by the way of reinforcement learning in consecutive detection, in which the complexity evaluation from sequential infrared images and results assessment of moving small target detection are integrated. The experiment results over four infrared small target image sequences with different cloudy sky backgrounds demonstrate the effectiveness and advantages of the proposed approach in both background clutter suppression and small target detection.

Figures in this Article
© 2016 SPIE and IS&T

Citation

Chuanyun Wang and Shiyin Qin
"Approach for moving small target detection in infrared image sequence based on reinforcement learning", J. Electron. Imaging. 25(5), 053032 (Oct 18, 2016). ; http://dx.doi.org/10.1117/1.JEI.25.5.053032


Access This Article
Sign in or Create a personal account to Buy this article ($20 for members, $25 for non-members).

Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging & repositioning the boxes below.

Related Book Chapters

Topic Collections

PubMed Articles
Advertisement
  • Don't have an account?
  • Subscribe to the SPIE Digital Library
  • Create a FREE account to sign up for Digital Library content alerts and gain access to institutional subscriptions remotely.
Access This Article
Sign in or Create a personal account to Buy this article ($20 for members, $25 for non-members).
Access This Proceeding
Sign in or Create a personal account to Buy this article ($15 for members, $18 for non-members).
Access This Chapter

Access to SPIE eBooks is limited to subscribing institutions and is not available as part of a personal subscription. Print or electronic versions of individual SPIE books may be purchased via SPIE.org.