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Journal of Electronic Imaging

Approach for moving small target detection in infrared image sequence based on reinforcement learning
Author(s): Chuanyun Wang; Shiyin Qin
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Paper 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.

Paper Details

Date Published: 18 October 2016
PDF: 12 pages
J. Electron. Imaging. 25(5) 053032 doi: 10.1117/1.JEI.25.5.053032
Published in: Journal of Electronic Imaging Volume 25, Issue 5
Show Author Affiliations
Chuanyun Wang, BeiHang Univ. (China)
Shenyang Aerospace Univ. (China)
Shiyin Qin, BeiHang Univ. (China)

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