Moment feature based fast feature extraction algorithm for moving object detection using aerial images

A. F M Saifuddin Saif, Anton Satria Prabuwono, Zainal Rasyid Mahayuddin

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

Fast and computationally less complex feature extraction for moving object detection using aerial images from unmanned aerial vehicles (UAVs) remains as an elusive goal in the field of computer vision research. The types of features used in current studies concerningmoving object detection are typically chosen based on improving detection rate rather than on providing fast and computationally less complex feature extraction methods. Because moving object detection using aerial images from UAVs involves motion as seen from a certain altitude, effective and fast feature extraction is a vital issue for optimum detection performance. This research proposes a two-layer bucket approach based on a new feature extraction algorithm referred to as the moment-based feature extraction algorithm (MFEA). Because a moment represents thecoherent intensity of pixels and motion estimation is a motion pixel intensity measurement, this research used this relation to develop the proposed algorithm. The experimental results reveal the successful performance of the proposed MFEA algorithm and the proposed methodology.

Original languageEnglish
Article numbere0126212
JournalPLoS One
Volume10
Issue number6
DOIs
Publication statusPublished - 1 Jun 2015

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Feature extraction
Antennas
Unmanned aerial vehicles (UAV)
Research
Pixels
buckets
computer vision
Motion estimation
Computer vision
Object detection
methodology
unmanned aerial vehicles

ASJC Scopus subject areas

  • Agricultural and Biological Sciences(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Medicine(all)

Cite this

Moment feature based fast feature extraction algorithm for moving object detection using aerial images. / Saif, A. F M Saifuddin; Prabuwono, Anton Satria; Mahayuddin, Zainal Rasyid.

In: PLoS One, Vol. 10, No. 6, e0126212, 01.06.2015.

Research output: Contribution to journalArticle

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