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Sensors 2017, 17(10), 2242;

Robust Small Target Co-Detection from Airborne Infrared Image Sequences

College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China
School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China
College of Software Engineering, Pingdingshan University, Pingdingshan 467000, China
Author to whom correspondence should be addressed.
Received: 14 August 2017 / Revised: 17 September 2017 / Accepted: 25 September 2017 / Published: 29 September 2017
(This article belongs to the Special Issue Video Analysis and Tracking Using State-of-the-Art Sensors)
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In this paper, a novel infrared target co-detection model combining the self-correlation features of backgrounds and the commonality features of targets in the spatio-temporal domain is proposed to detect small targets in a sequence of infrared images with complex backgrounds. Firstly, a dense target extraction model based on nonlinear weights is proposed, which can better suppress background of images and enhance small targets than weights of singular values. Secondly, a sparse target extraction model based on entry-wise weighted robust principal component analysis is proposed. The entry-wise weight adaptively incorporates structural prior in terms of local weighted entropy, thus, it can extract real targets accurately and suppress background clutters efficiently. Finally, the commonality of targets in the spatio-temporal domain are used to construct target refinement model for false alarms suppression and target confirmation. Since real targets could appear in both of the dense and sparse reconstruction maps of a single frame, and form trajectories after tracklet association of consecutive frames, the location correlation of the dense and sparse reconstruction maps for a single frame and tracklet association of the location correlation maps for successive frames have strong ability to discriminate between small targets and background clutters. Experimental results demonstrate that the proposed small target co-detection method can not only suppress background clutters effectively, but also detect targets accurately even if with target-like interference. View Full-Text
Keywords: infrared background; target extraction; target refinement; small target co-detection infrared background; target extraction; target refinement; small target co-detection

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Gao, J.; Wen, C.; Liu, M. Robust Small Target Co-Detection from Airborne Infrared Image Sequences. Sensors 2017, 17, 2242.

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