An automatic multi-target independent analysis framework for non-planar infrared-visible registration

Xinglong Sun, Tingfa Xu*, Jizhou Zhang, Zishu Zhao, Yuankun Li

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)

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.

Original languageEnglish
Article number1696
JournalSensors
Volume17
Issue number8
DOIs
Publication statusPublished - Aug 2017

Keywords

  • Feature matching
  • Gaussian criterion
  • Infrared-visible videos
  • Multi-target registration
  • Multi-target tracking
  • Non-planar

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