A Novel Motion Detection Method Using 3D Discrete Wavelet Transform

Sahar Yousefi, M.T. Manzuri Shalmani, Jeremy Lin, Marius Staring

Research output: Contribution to journalArticleScientificpeer-review

3 Citations (Scopus)
32 Downloads (Pure)

Abstract

The problem of motion detection has received considerable attention due to the explosive growth of its applications in video analysis and surveillance systems. While the previous approaches can produce good results, the accurate detection of motion remains a challenging task due to the difficulties raised by illumination variations, occlusion, camouflage, sudden motions appearing in burst, dynamic texture, and environmental changes such as those on weather conditions, sunlight changes during a day, etc. In this study, a novel per-pixel motion descriptor is proposed for motion detection in video sequences which outperforms the current methods in the literature particularly in severe scenarios. The proposed descriptor is based on two complementary three-dimensional discrete wavelet transforms (3D-DWT) and a three-dimensional wavelet leader. In this approach, a feature vector is extracted for each pixel by applying a novel three-dimensional wavelet-based motion descriptor. Then, the extracted features are clustered by the well-known K-means algorithm. The experimental results demonstrate the effectiveness of the proposed method compared to state-of-the-art approaches in several public benchmark datasets. The application of the proposed method and additional experimental results for several challenging datasets are available online.

Original languageEnglish
Article number8561242
Pages (from-to)3487-3500
Number of pages14
JournalIEEE Transactions on Circuits and Systems for Video Technology
Volume29
Issue number12
DOIs
Publication statusPublished - 1 Dec 2019

Bibliographical note

This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication.

Keywords

  • 3D-discrete Wavelet Transform
  • Dynamic texture
  • Motion detection
  • Wavelet leader

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