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Sensors 2017, 17(8), 1696; https://doi.org/10.3390/s17081696

An Automatic Multi-Target Independent Analysis Framework for Non-Planar Infrared-Visible Registration

1
Image Engineering & Video Technology Lab, School of Optoelectronics, Beijing Institute of Technology, Beijing 100081, China
2
Key Laboratory of Photoelectronic Imaging Technology and Systems, Ministry of Education of China, Beijing 100081, China
*
Author to whom correspondence should be addressed.
Received: 12 June 2017 / Revised: 11 July 2017 / Accepted: 21 July 2017 / Published: 26 July 2017
(This article belongs to the Section Physical Sensors)
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Abstract

In this paper, we propose a novel automatic multi-target registration framework for non-planar infrared-visible videos. Previous approaches usually analyzed multiple targets together and then estimated a global homography for the whole scene, however, these cannot achieve precise multi-target registration when the scenes are non-planar. Our framework is devoted to solving the problem using feature matching and multi-target tracking. The key idea is to analyze and register each target independently. We present a fast and robust feature matching strategy, where only the features on the corresponding foreground pairs are matched. Besides, new reservoirs based on the Gaussian criterion are created for all targets, and a multi-target tracking method is adopted to determine the relationships between the reservoirs and foreground blobs. With the matches in the corresponding reservoir, the homography of each target is computed according to its moving state. We tested our framework on both public near-planar and non-planar datasets. The results demonstrate that the proposed framework outperforms the state-of-the-art global registration method and the manual global registration matrix in all tested datasets. View Full-Text
Keywords: multi-target registration; infrared-visible videos; non-planar; feature matching; Gaussian criterion; multi-target tracking multi-target registration; infrared-visible videos; non-planar; feature matching; Gaussian criterion; multi-target tracking
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Sun, X.; Xu, T.; Zhang, J.; Zhao, Z.; Li, Y. An Automatic Multi-Target Independent Analysis Framework for Non-Planar Infrared-Visible Registration. Sensors 2017, 17, 1696.

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